---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/log_appendix.log
  log type:  text
 opened on:  10 Feb 2024, 12:03:48

. 
. *----- 
. *-------------------------------
. *---  C. Survey design. Sample sizes  (Table C1)
. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. matrix EV = J(9,3,0)

. matrix colnames EV = "Likert" "Likert +" "QVSR" 

. matrix rownames EV = "Observations (dup incl)" ///
> "Individuals (dup excl)" /// 
> "Duplicate rate" ///
> "Donation task (dup excluded)" /// 
> "Gender (dup excluded)" /// 
> "Drop out rate (as a share of ind.)" ///
> "Loss rate (as a share of obs.)" ///
> "DG (dup excl)" ///
> "Letter Writing (dup excl)" 

. 
. 
. 
. *-- FIRST ROW: wave 1, how many observations do we start with , duplicates included?
. use "$pathdata/part1_wave1_qualtrics.dta", replace

. ** destring
. destring block, replace
block: all characters numeric; replaced as int
(38 missing values generated)

. destring treatment, replace
treatment: all characters numeric; replaced as int

. destring id, replace
id: all characters numeric; replaced as long
(2 missing values generated)

. 
. **- rename 
. rename I_Session session

. 
. **- drop if treatment == 7 [a small subset of respondents received a different version of QVSR, this was a pilot used for a separate study, we consequently drop these observations]
. drop if treatment == 7 
(471 observations deleted)

. 
. **- generate treatment variable [within each treatment branch, we further randomised respondents into receiving versus not receiving a partisan prime. Our treatment failed to manipulate partisan identity and had no effect on policy preferences. We thus 
> pool primed and non-primed observations.]
. gen method = treatment

. recode method (1 4 = 1) ( 2 5 = 2) ( 3 6 = 3) 
(1,400 changes made to method)

. 
. label variable method   "Survey method treatment"

. label define method_lbl 1   "Likert", add

. label define method_lbl 2   "Likert+", add

. label define method_lbl 3   "QVSR", add

. label define method_lbl 999   "Dropped out before being assigned to treatment", add

. label values method method_lbl

. 
. **- drop if dropped out before being assigned to treatment 
. drop if method == 999
(787 observations deleted)

. 
. 
. **- drop observations (14 in total) generated by authors during last minute sanity check 
. 
. 
. **- test, no id
. drop if id == .
(0 observations deleted)

. **- CC's birthdate
. drop if id == 13041984
(4 observations deleted)

. **- if over a billion
. drop if id > 1000000000
(3 observations deleted)

. 
. 
. gen ICN = .
(4,183 missing values generated)

. replace ICN = 1 if ICN_Q2 == 1
(4,180 real changes made)

. replace ICN = 0 if ICN_Q2 == 2
(3 real changes made)

. 
. label variable ICN      "Informed consent page"

. label define ICN_lbl 1   "Consented", add

. label define ICN_lbl 0   "Did not consent", add

. label values ICN ICN_lbl

. 
. 
. ** Number of observations (conditional on consenting), this includes duplicates
. tab ICN if method == 1 & ICN == 1

       Informed |
   consent page |      Freq.     Percent        Cum.
----------------+-----------------------------------
      Consented |      1,392      100.00      100.00
----------------+-----------------------------------
          Total |      1,392      100.00

. matrix EV[1,1] = r(N)

. local consent1 = r(N)

. tab ICN if method == 2 & ICN == 1

       Informed |
   consent page |      Freq.     Percent        Cum.
----------------+-----------------------------------
      Consented |      1,391      100.00      100.00
----------------+-----------------------------------
          Total |      1,391      100.00

. matrix EV[1,2] = r(N)

. local consent2 = r(N)

. tab ICN if method == 3 & ICN == 1

       Informed |
   consent page |      Freq.     Percent        Cum.
----------------+-----------------------------------
      Consented |      1,397      100.00      100.00
----------------+-----------------------------------
          Total |      1,397      100.00

. matrix EV[1,3] = r(N)

. local consent3 = r(N)

. 
. 
. sort id session StartDate

. quietly by id : gen n = _n

. 
. quietly bys id : gen dup = cond(_N==1,0,_n)

. tab dup n 

           |                           n
       dup |         1          2          3          4          5 |     Total
-----------+-------------------------------------------------------+----------
         0 |     3,901          0          0          0          0 |     3,901 
         1 |       133          0          0          0          0 |       133 
         2 |         0        133          0          0          0 |       133 
         3 |         0          0         13          0          0 |        13 
         4 |         0          0          0          2          0 |         2 
         5 |         0          0          0          0          1 |         1 
-----------+-------------------------------------------------------+----------
     Total |     4,034        133         13          2          1 |     4,183 

. 
. quietly bys id : gen dupF = cond(_N==1,0,_n)

. tab dupF 

       dupF |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      3,901       93.26       93.26
          1 |        133        3.18       96.44
          2 |        133        3.18       99.62
          3 |         13        0.31       99.93
          4 |          2        0.05       99.98
          5 |          1        0.02      100.00
------------+-----------------------------------
      Total |      4,183      100.00

. 
. *-- SECOND ROW: wave 1, how many unique individuals, conditional on consenting 
. tab ICN if method == 1 & ICN == 1 & dupF < 2

       Informed |
   consent page |      Freq.     Percent        Cum.
----------------+-----------------------------------
      Consented |      1,357      100.00      100.00
----------------+-----------------------------------
          Total |      1,357      100.00

. matrix EV[2,1] = r(N)

. local consenta1 = r(N)

. tab ICN if method == 2 & ICN == 1 & dupF < 2

       Informed |
   consent page |      Freq.     Percent        Cum.
----------------+-----------------------------------
      Consented |      1,349      100.00      100.00
----------------+-----------------------------------
          Total |      1,349      100.00

. matrix EV[2,2] = r(N)

. local consenta2 = r(N)

. tab ICN if method == 3 & ICN == 1 & dupF < 2

       Informed |
   consent page |      Freq.     Percent        Cum.
----------------+-----------------------------------
      Consented |      1,325      100.00      100.00
----------------+-----------------------------------
          Total |      1,325      100.00

. matrix EV[2,3] = r(N)

. local consenta3 = r(N)

. 
. 
. *-- THIRD ROW: wave 1, duplicate rate 
. matrix EV[3,1] = ((`consent1'-`consenta1')/`consent1') * 100

. matrix EV[3,2] = ((`consent2'-`consenta2')/`consent2' ) * 100

. matrix EV[3,3] = ((`consent3'-`consenta3')/`consent3' ) * 100

. 
. 
. 
. *-- FOURTH and FITH ROWS: number of observations used for the main analyses (e.g., Fig2 and Fig4), focusing on wave 1
. use "$pathout/dataset_final.dta", replace

. 
. ** flag observations used in the main donation analysis  (Fig 2 in main manuscript)
. gen comp_bev_w1 = 0

. replace comp_bev_w1 = 1 if votes_AAw1 < . & votes_gunw1 < . & votes_wallw1 < . & votes_paidLw1 < . & ///
> votes_genderw1 < . & votes_gayw1 < . & votes_minWw1 < . & votes_abortionw1 < . & votes_deficitw1 < . & ///
> votes_envirow1 < . & don_C_gun < . 
(3,679 real changes made)

. 
. tab comp_bev_w1 if method == 1 & comp_bev_w1 == 1 

comp_bev_w1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,257      100.00      100.00
------------+-----------------------------------
      Total |      1,257      100.00

. matrix EV[4,1] = r(N)

. tab comp_bev_w1 if method == 2 & comp_bev_w1 == 1 

comp_bev_w1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,259      100.00      100.00
------------+-----------------------------------
      Total |      1,259      100.00

. matrix EV[4,2] = r(N)

. tab comp_bev_w1 if method == 3 & comp_bev_w1 == 1 

comp_bev_w1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,163      100.00      100.00
------------+-----------------------------------
      Total |      1,163      100.00

. matrix EV[4,3] = r(N)

. 
. ** flag observations used in the main exposure analysis (Fig 4 and 5 in main manuscript)
. gen comp_exp_w1 = 0

. replace comp_exp_w1 = 1 if votes_AAw1 < . & votes_gunw1 < . & votes_wallw1 < . & votes_paidLw1 < . & ///
> votes_genderw1 < . & votes_gayw1 < . & votes_minWw1 < . & votes_abortionw1 < . & votes_deficitw1 < . & ///
> votes_envirow1 < . & sex < . 
(3,917 real changes made)

. 
. tab comp_exp_w1 if method == 1 & comp_exp_w1 == 1 

comp_exp_w1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,333      100.00      100.00
------------+-----------------------------------
      Total |      1,333      100.00

. matrix EV[5,1] = r(N)

. local used1 = r(N)

. tab comp_exp_w1 if method == 2 & comp_exp_w1 == 1 

comp_exp_w1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,333      100.00      100.00
------------+-----------------------------------
      Total |      1,333      100.00

. matrix EV[5,2] = r(N)

. local used2 = r(N)

. tab comp_exp_w1 if method == 3 & comp_exp_w1 == 1 

comp_exp_w1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,251      100.00      100.00
------------+-----------------------------------
      Total |      1,251      100.00

. matrix EV[5,3] = r(N)

. local used3 = r(N)

. 
. 
. *-- SIXTH ROW: How many unique  individuals drop out 
. matrix EV[6,1] = (1 - (`used1'/`consenta1')) * 100

. matrix EV[6,2] = (1 - (`used2'/`consenta2')) * 100

. matrix EV[6,3] = (1 - (`used3'/`consenta3')) * 100

. 
. 
. *-- SEVENTH ROW: How many attempts at taking the survey do not produce a usable observations
. matrix EV[7,1] = (1 - (`used1'/`consent1')) * 100

. matrix EV[7,2] = (1 - (`used2'/`consent2')) * 100

. matrix EV[7,3] = (1 - (`used3'/`consent3')) * 100

. 
. *--- EIGHTH AND NINETH ROWS: number of observations used for the main analyses (e.g., Fig3), focusing on wave 2
. ** flag observations used in the DG analysis  (Fig 3 in main manuscript)
. gen comp_DG_w2 = 0

. replace comp_DG_w2 = 1 if votes_AAw1 < . & votes_gunw1 < . & votes_wallw1 < . & votes_paidLw1 < . & ///
> votes_genderw1 < . & votes_gayw1 < . & votes_minWw1 < . & votes_abortionw1 < . & votes_deficitw1 < . & ///
> votes_envirow1 < . & punish_FaST < . 
(1,538 real changes made)

. 
. 
. tab comp_DG_w2 if method == 1 & comp_DG_w2 == 1

 comp_DG_w2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        513      100.00      100.00
------------+-----------------------------------
      Total |        513      100.00

. matrix EV[8,1] = r(N)

. tab comp_DG_w2 if method == 2 & comp_DG_w2 == 1

 comp_DG_w2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        532      100.00      100.00
------------+-----------------------------------
      Total |        532      100.00

. matrix EV[8,2] = r(N)

. tab comp_DG_w2 if method == 3 & comp_DG_w2 == 1

 comp_DG_w2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        493      100.00      100.00
------------+-----------------------------------
      Total |        493      100.00

. matrix EV[8,3] = r(N)

. 
. 
. ** flag observations used in the letter writing analysis (Fig 3 in main manuscript)
. gen comp_write_w2 = 0

. replace comp_write_w2 = 1 if votes_AAw1 < . & votes_gunw1 < . & votes_wallw1 < . & votes_paidLw1 < . & ///
> votes_genderw1 < . & votes_gayw1 < . & votes_minWw1 < . & votes_abortionw1 < . & votes_deficitw1 < . & ///
> votes_envirow1 < . & wrote < . 
(1,566 real changes made)

. 
. 
. tab comp_write_w2 if method == 1 & comp_write_w2 == 1

comp_write_ |
         w2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        518      100.00      100.00
------------+-----------------------------------
      Total |        518      100.00

. matrix EV[9,1] = r(N)

. tab comp_write_w2 if method == 2 & comp_write_w2 == 1

comp_write_ |
         w2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        540      100.00      100.00
------------+-----------------------------------
      Total |        540      100.00

. matrix EV[9,2] = r(N)

. tab comp_write_w2 if method == 3 & comp_write_w2 == 1

comp_write_ |
         w2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        508      100.00      100.00
------------+-----------------------------------
      Total |        508      100.00

. matrix EV[9,3] = r(N)

. 
. 
. 
. 
. cd "$pathtab"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/tab

. 
. *****************
. 
. esttab  matrix(EV, fmt("0 0")) using tab_C1.tex, replace
(output written to tab_C1.tex)

. 
. **********************************************************
. * OUTPUT TAB C1.                                         *
. * PLEASE RUN CORRESPONDING LATEX FILE AVAILABLE IN "TAB" *
. **********************************************************
. 
. 
. *----- 
. *-------------------------------
. *---  C. Survey design. balance tables (Table C2 - C3 and C4)
. 
. 
. use "$pathout/dataset_final.dta", replace

. 
. gen comp_bev_w1 = 0

. replace comp_bev_w1 = 1 if votes_AAw1 < . & votes_gunw1 < . & votes_wallw1 < . & votes_paidLw1 < . & ///
> votes_genderw1 < . & votes_gayw1 < . & votes_minWw1 < . & votes_abortionw1 < . & votes_deficitw1 < . & ///
> votes_envirow1 < . & don_C_gun < . 
(3,679 real changes made)

. 
. keep if comp_bev_w1 ==1
(285 observations deleted)

. tab method, gen(meth)

                Survey method treatment |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                 Likert |      1,257       34.17       34.17
                                Likert+ |      1,259       34.22       68.39
                                   QVSR |      1,163       31.61      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,679      100.00

. tab xparty7, gen(prty)

                   Party ID |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
          Strong Republican |        623       17.03       17.03
      Not Strong Republican |        460       12.57       29.60
           Leans Republican |        634       17.33       46.93
Undecided/Independent/Other |         64        1.75       48.67
             Leans Democrat |        591       16.15       64.83
        Not Strong Democrat |        456       12.46       77.29
            Strong Democrat |        831       22.71      100.00
----------------------------+-----------------------------------
                      Total |      3,659      100.00

. recode ppeducat 1=2
(142 changes made to ppeducat)

. tab ppeducat, gen(educ)

    Education (Categorical) |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
                High school |      1,062       29.02       29.02
               Some college |      1,126       30.77       59.80
Bachelor's degree or higher |      1,471       40.20      100.00
----------------------------+-----------------------------------
                      Total |      3,659      100.00

. recode gunOa (2 3 = 0) (4 = 1)
(3,659 changes made to gunOa)

. recode ppethm (5 =3)
(112 changes made to ppethm)

. tab ppethm, gen(ethnic)

      Race / Ethnicity |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
   White, Non-Hispanic |      2,767       75.62       75.62
   Black, Non-Hispanic |        321        8.77       84.39
   Other, Non-Hispanic |        250        6.83       91.23
              Hispanic |        321        8.77      100.00
-----------------------+-----------------------------------
                 Total |      3,659      100.00

. recode minWa (1=0) (2 3 = 1)
(2,286 changes made to minWa)

. replace minWa = 99 if minWa == . 
(1,393 real changes made)

. tab minWa, gen(MW)

      minWa |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,330       36.15       36.15
          1 |        956       25.99       62.14
         99 |      1,393       37.86      100.00
------------+-----------------------------------
      Total |      3,679      100.00

. ** child and gun all good parents_immi gay, not missing many_ ex: tab gunOa comp_exp_w1, miss
. replace Nevangelical = 99 if Nevangelical == . 
(1,127 real changes made)

. tab Nevangelical, gen(BA)

  RECODE of |
evangelical |
 (RECODE of |
  xppp20071 |
     (Q26A: |
  Would you |
   describe |
yourself as |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        957       26.01       26.01
          1 |      1,595       43.35       69.37
         99 |      1,127       30.63      100.00
------------+-----------------------------------
      Total |      3,679      100.00

. replace evangelical = (evangelical * -1) +3
(2,552 real changes made)

. 
. 
. rename don_C_gun Donation_gun

. rename don_C_wall_in Donation_immi

. rename xparty7 Party_ID

. rename xideo Ideology

. rename educ1 HS_or_less

. rename educ2 Some_college

. rename educ3 BA

. rename ppage Age

. rename sex Gender

. rename ethnic1 White

. rename ethnic2 Black

. rename ethnic3 Other

. rename ethnic4 Hispanic

. rename MW2 Exp_minW

. rename MW3 MissV_minW

. rename child2 Paid_leave_exp

. rename gunOa Gun_exp

. rename parents_immi Immi_exp

. rename gay Sexual_orientation

. rename evangelical Born_again

. rename BA3 MissV_BA

. 
. balancetable meth1 Donation_gun Donation_immi Party_ID Ideology HS_or_less ///
>  Some_college BA Age Gender White Black Other Hispanic ///
>  Exp_minW MissV_minW Paid_leave_exp Gun_exp Immi_exp Sexual_orientation ///
>  Born_again MissV_BA ///
>  using "bal1.tex",  ctitles("Likert+/QVSR (pooled)" "Likert" "Difference") replace

. 
. balancetable meth2 Donation_gun Donation_immi Party_ID Ideology HS_or_less ///
>  Some_college BA Age Gender White Black Other Hispanic ///
>  Exp_minW MissV_minW Paid_leave_exp Gun_exp Immi_exp Sexual_orientation ///
>  Born_again MissV_BA ///
>  using "bal2.tex",  ctitles("Likert/QVSR (pooled)" "Likert+" "Difference") replace

. 
. balancetable meth3 Donation_gun Donation_immi Party_ID Ideology HS_or_less ///
>  Some_college BA Age Gender White Black Other Hispanic ///
>  Exp_minW MissV_minW Paid_leave_exp Gun_exp Immi_exp Sexual_orientation ///
>  Born_again MissV_BA ///
>  using "bal3.tex",  ctitles("Likert/Likert+ (pooled)" "QVSR" "Difference") replace

. 
. 
. 
. 
. ***********************************************************
. * OUTPUT TAB C2, TAB C3 and TAB C4                        *
. * PLEASE RUN CORRESPONDING LATEX FILES AVAILABLE IN "TAB" *
. ***********************************************************
. 
. 
. 
. 
. *----- 
. *-------------------------------
. *---  C. Survey design. Attrition, Table C5
. 
. 
. ***-- build a dataset of all observations who match following conditions:
. * consented
. * finished part 1, meaning saw the items on proximity to childbirth and minwage
. ***-- then match to SES variables provided by IPSOS
. 
. 
. 
. use "$pathdata/part1_wave1_qualtrics.dta", replace

. ** destring
. destring block, replace
block: all characters numeric; replaced as int
(38 missing values generated)

. destring treatment, replace
treatment: all characters numeric; replaced as int

. destring id, replace
id: all characters numeric; replaced as long
(2 missing values generated)

. 
. **- rename 
. rename I_Session session

. 
. **- drop if treatment == 7 [a small subset of respondents received a different version of QVSR, this was a pilot used for a separate study, we consequently drop these observations]
. drop if treatment == 7 
(471 observations deleted)

. 
. **- generate treatment variable [within each treatment branch, we further randomised respondents into receiving versus not receiving a partisan prime. Our treatment failed to manipulate partisan identity and had no effect on policy preferences. We thus 
> pool primed and non-primed observations.]
. gen method = treatment

. recode method (1 4 = 1) ( 2 5 = 2) ( 3 6 = 3) 
(1,400 changes made to method)

. 
. label variable method   "Survey method treatment"

. label define method_lbl 1   "Likert", add

. label define method_lbl 2   "Likert+", add

. label define method_lbl 3   "QVSR", add

. label define method_lbl 999   "Dropped out before being assigned to treatment", add

. label values method method_lbl

. 
. **- drop if dropped out before being assigned to treatment 
. drop if method == 999
(787 observations deleted)

. 
. 
. **- drop observations (14 in total) generated by authors during last minute sanity check 
. 
. 
. **- test, no id
. drop if id == .
(0 observations deleted)

. **- CC's birthdate
. drop if id == 13041984
(4 observations deleted)

. **- if over a billion
. drop if id > 1000000000
(3 observations deleted)

. 
. 
. gen ICN = .
(4,183 missing values generated)

. replace ICN = 1 if ICN_Q2 == 1
(4,180 real changes made)

. replace ICN = 0 if ICN_Q2 == 2
(3 real changes made)

. 
. label variable ICN      "Informed consent page"

. label define ICN_lbl 1   "Consented", add

. label define ICN_lbl 0   "Did not consent", add

. label values ICN ICN_lbl

. 
. **- keep if consented
. keep if ICN == 1 
(3 observations deleted)

. 
. 
. **-- flag duplicates 
. 
. quietly bys id : gen dup = cond(_N==1,0,_n)

. 
. recode dup ( 1/10 = 1), gen(restart)
(149 differences between dup and restart)

. 
. tab restart method, col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

 RECODE of |     Survey method treatment
       dup |    Likert    Likert+       QVSR |     Total
-----------+---------------------------------+----------
         0 |     1,319      1,309      1,270 |     3,898 
           |     94.76      94.10      90.91 |     93.25 
-----------+---------------------------------+----------
         1 |        73         82        127 |       282 
           |      5.24       5.90       9.09 |      6.75 
-----------+---------------------------------+----------
     Total |     1,392      1,391      1,397 |     4,180 
           |    100.00     100.00     100.00 |    100.00 

. 
. **--- match to SES 
. 
. 
. destring xideo xpppa1690, replace
xideo: all characters numeric; replaced as byte
(30 missing values generated)
xpppa1690: all characters numeric; replaced as byte
(30 missing values generated)

. 
. merge m:1 id using "$pathdata/ses_ipsos.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                         3,871
        from master                        31  (_merge==1)
        from using                      3,840  (_merge==2)

    Matched                             4,149  (_merge==3)
    -----------------------------------------

. drop if _merge == 2
(3,840 observations deleted)

. drop _merge 

. 
. **-- code predictors 
. do "$pathcode/0.code_var_SES_F.do"

. **----------------------------
. *** SES
. 
. 
. destring pid3, replace
pid3: all characters numeric; replaced as byte
(31 missing values generated)

. 
. 
. 
. *** white versus blak
. 
. recode ppethm( 1=1) ( 2 3 4 5 = 0), gen(white)
(1,021 differences between ppethm and white)

. recode ppethm( 1=0) ( 2  5 = 1) (3 4 = .), gen(black_W)
(4,149 differences between ppethm and black_W)

. 
. 
. *** latino / immigrant 
. recode ppethm( 1=0) ( 2 3 5 = .) (4 = 1), gen(latino_W)
(4,149 differences between ppethm and latino_W)

. 
. 
. recode minWS1 (2 3 4 = 0), gen(working)
(1,779 differences between minWS1 and working)

. *** 
. 
. gen minW_hourly = .
(4,180 missing values generated)

. *** hourly wage peeps, currently working 
. replace minW_hourly = 1 if working ==  1 &  minWQ2a == 1 
(16 real changes made)

. replace minW_hourly = 1 if working ==  1 &  minWQ3a == 16 
(136 real changes made)

. replace minW_hourly = 1 if working ==  1 &  minWQ4a == 1
(262 real changes made)

. replace minW_hourly = 2 if working ==  1 &  minWQ5a == 1
(223 real changes made)

. replace minW_hourly =3 if working ==  1 &  minWQ5a == 2
(490 real changes made)

. 
. *** hourly wage peeps, currently not working 
. replace minW_hourly = 1 if working ==  0 &  minWQ2b == 1 
(5 real changes made)

. replace minW_hourly = 1 if working ==  0 &  minWQ3b == 16 
(49 real changes made)

. replace minW_hourly = 1 if working ==  0 &  minWQ4b == 1
(31 real changes made)

. replace minW_hourly = 2 if working ==  0 &  minWQ5b == 1
(12 real changes made)

. replace minW_hourly =3 if working ==  0 &  minWQ5b == 2
(14 real changes made)

. 
. *** hourly wage peeps, currently not working but someone in HH
. replace minW_hourly = 1 if working ==  0 &  minW_Q2c == 1 
(2 real changes made)

. replace minW_hourly = 1 if working ==  0 &  minW_Q3c == 16 
(36 real changes made)

. replace minW_hourly = 1 if working ==  0 &  minW_Q4c == 1
(53 real changes made)

. replace minW_hourly = 2 if working ==  0 &  minW_Q5c == 1
(39 real changes made)

. replace minW_hourly =3 if working ==  0 &  minW_Q5c == 2
(97 real changes made)

. 
. 
. *** minW_hourly2
. ** = 1 if below $15/h
. ** = 2 if right above 15$/h
. ** = 3 if much above 15$/h
. 
. *** salary peeps
. gen minW_hourly2 = minW_hourly
(2,715 missing values generated)

. replace minW_hourly2 = 1 if working ==  1 &  minWsal1a == 1 
(79 real changes made)

. replace minW_hourly2 = 2 if working ==  1 &  minWsal2a == 1 
(92 real changes made)

. replace minW_hourly2 = 3 if working ==  1 &  minWsal2a == 2
(755 real changes made)

. 
. replace minW_hourly2 = 1 if working ==  0 &  minW_sal1b == 1 
(0 real changes made)

. replace minW_hourly2 = 2 if working ==  0 &  minW_sal2b == 1 
(0 real changes made)

. replace minW_hourly2 = 3 if working == 0 &  minW_sal2b == 2
(0 real changes made)

. 
. replace minW_hourly2 = 1 if working ==  0 &  minW_sal1c == 1 
(13 real changes made)

. replace minW_hourly2 = 2 if working ==  0 &  minW_sal2c == 1 
(20 real changes made)

. replace minW_hourly2 = 3 if working ==  0 &  minW_sal2c == 2
(120 real changes made)

. 
. *** generate a common variable for both an indicator for salary/hourly/nobody working
. 
. gen minW = minW_hourly
(2,715 missing values generated)

. replace minW = minW_hourly2 if minW == . 
(1,079 real changes made)

. 
. gen type_minW = .
(4,180 missing values generated)

. replace type_minW = 0 if minW_hourly2 < .
(2,544 real changes made)

. replace type_minW = 1 if minW_hourly < .
(1,465 real changes made)

. 
. 
. 
. **** 
. gen minW_hourly2a = (-1 *minW_hourly2) + 4
(1,636 missing values generated)

. gen minW_hourlya = (-1 *minW_hourly) + 4
(2,715 missing values generated)

. 
. gen minWa = minW_hourlya
(2,715 missing values generated)

. replace minWa = minW_hourly2a if minWa == . 
(1,079 real changes made)

. 
. 
. 
. 
. 
. 
. ***** gun , = 2 if gun in the house and own it
. **, = 1 if gun in the house but does not own it
. ** = 0 if no gun in the house 
. 
. gen gunO = 0 if xppp20117 == 2
(1,611 missing values generated)

. replace gunO = 2 if xppp20117 == 1 & xppp20122 == 1
(1,118 real changes made)

. replace gunO = 1 if xppp20117 == 1 & xppp20122 == 2
(462 real changes made)

. 
. gen gunOa = (-1*gunO)+ 4
(31 missing values generated)

. 
. 
. 
. ** gay
. 
. recode xppalgb ( 1 3 4 = 1) (2 = 0), gen(gay)
(4,019 differences between xppalgb and gay)

. 
. *** vote
. 
. recode xpppa1690 ( 1=1) (2=2) (3= 3) ( 4 5 = 4), gen(vote16)
(710 differences between xpppa1690 and vote16)

. 
. 
. *** gender
. 
. rename ppgender sex

. 
. 
. *** ethnicty 
. 
. gen whitea = 0 if white == 1
(1,052 missing values generated)

. replace whitea = 1 if white == 0
(1,021 real changes made)

. 
. 
. *** party id
. 
. recode xparty7 ( 1 /3 = 0) (4 = .) (5/7 = 1), gen(dem)
(4,149 differences between xparty7 and dem)

. 
. 
. 
. *** religion 
. recode xppp20071 ( 3 = .) (1=3), gen(evangelical)
(2,333 differences between xppp20071 and evangelical)

. recode evangelical ( 3 = 0) ( 2  = 1) , gen(Nevangelical)
(2,908 differences between evangelical and Nevangelical)

. 
. 
. *** reli2, add attendance
. ** = 3 if evangelical who goes once a week or more
. gen reli2 = 3 if evangelical == 3 & XREL2 < 3
(3,519 missing values generated)

. replace reli2 = 2 if evangelical == 3 & XREL2 == 3 
(91 real changes made)

. replace reli2 = 1 if evangelical == 3 & XREL2 == 4 
(168 real changes made)

. replace reli2 = 1 if evangelical == 3 & XREL2 == 5
(111 real changes made)

. replace reli2 = 1 if evangelical == 3 & XREL2 == 6
(61 real changes made)

. replace reli2 = 0 if evangelical == 2
(1,816 real changes made)

. replace reli2 = -1 if evangelical == 2 & XREL2 < 3
(533 real changes made)

. 
. 
. 
. *** children
. 
. ** do you want children?
. gen childA = .
(4,180 missing values generated)

. replace childA = 5 if childQ4 == 1 |  childQ3 == 1
(286 real changes made)

. replace childA = 0 if childQ4 == 3 |  childQ3 == 3
(3,362 real changes made)

. replace childA = 2 if childQ4 == 4 |  childQ3 == 4
(318 real changes made)

. replace childA = 4 if childQ4 == 2 |  childQ3 == 2
(208 real changes made)

. 
. ** do you have children?
. gen childB = .
(4,180 missing values generated)

. replace childB = 4 if childQ2 == 1 |  childQ2 == 3
(390 real changes made)

. replace childB = 3 if childQ2 == 4 
(240 real changes made)

. replace childB = 2 if childQ2 == 5 
(396 real changes made)

. replace childB = 1 if childQ2 == 6 
(1,906 real changes made)

. replace childB = 0 if childQ1 == 2
(1,244 real changes made)

. 
. gen child = childA + childB
(6 missing values generated)

. 
. recode child ( 0 1 = 1) ( 2 3 = 2) ( 4 5 6 7 8 9 = 3), gen(child2)
(1,730 differences between child and child2)

. 
. *** income /hardship
. factor black_W pprent ppincimp pphouse ppeducat
(obs=3,624)

Factor analysis/correlation                      Number of obs    =      3,624
    Method: principal factors                    Retained factors =          2
    Rotation: (unrotated)                        Number of params =          9

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      1.25919      0.97141            1.1660       1.1660
        Factor2  |      0.28778      0.30176            0.2665       1.4325
        Factor3  |     -0.01398      0.19218           -0.0129       1.4195
        Factor4  |     -0.20616      0.04075           -0.1909       1.2286
        Factor5  |     -0.24691            .           -0.2286       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(10) = 2470.75 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
         black_W |   0.2269    0.1012 |      0.9383  
          pprent |   0.5661    0.2201 |      0.6311  
        ppincimp |  -0.6428    0.1987 |      0.5473  
         pphouse |   0.5526    0.2361 |      0.6389  
        ppeducat |  -0.4107    0.3659 |      0.6975  
    -------------------------------------------------

. predict hardship1
(option regression assumed; regression scoring)

Scoring coefficients (method = regression)

    ----------------------------------
        Variable |  Factor1   Factor2 
    -------------+--------------------
         black_W |  0.08442   0.07873 
          pprent |  0.27979   0.21986 
        ppincimp | -0.36245   0.21962 
         pphouse |  0.26947   0.23358 
        ppeducat | -0.17777   0.32521 
    ----------------------------------


.  
. factor black_W pprent ppincimp pphouse ppeducat minW_hourly2
(obs=2,166)

Factor analysis/correlation                      Number of obs    =      2,166
    Method: principal factors                    Retained factors =          2
    Rotation: (unrotated)                        Number of params =         11

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      1.63251      1.14080            1.0276       1.0276
        Factor2  |      0.49171      0.49520            0.3095       1.3371
        Factor3  |     -0.00350      0.10034           -0.0022       1.3349
        Factor4  |     -0.10384      0.10647           -0.0654       1.2696
        Factor5  |     -0.21031      0.00763           -0.1324       1.1372
        Factor6  |     -0.21795            .           -0.1372       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(15) = 2353.76 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
         black_W |  -0.2225    0.1186 |      0.9364  
          pprent |  -0.5272    0.3559 |      0.5954  
        ppincimp |   0.7479    0.1416 |      0.4207  
         pphouse |  -0.4973    0.3810 |      0.6076  
        ppeducat |   0.4320    0.3296 |      0.7047  
    minW_hourly2 |   0.5585    0.2778 |      0.6110  
    -------------------------------------------------

. predict hardship2
(option regression assumed; regression scoring)

Scoring coefficients (method = regression)

    ----------------------------------
        Variable |  Factor1   Factor2 
    -------------+--------------------
         black_W | -0.06171   0.07784 
          pprent | -0.21207   0.30843 
        ppincimp |  0.41962   0.15608 
         pphouse | -0.19581   0.32164 
        ppeducat |  0.14043   0.23980 
    minW_hourly2 |  0.21091   0.22640 
    ----------------------------------


. 
. 
. *** immi
. 
. recode  immiQ1 ( 1 2 3 = 0) (4 = 1), gen(FB)
(4,174 differences between immiQ1 and FB)

. recode immiQ2 (4 = .), gen(parents_immi)
(13 differences between immiQ2 and parents_immi)

. recode immiQ3 (6 = .), gen(Gparents_immi)
(249 differences between immiQ3 and Gparents_immi)

. 
. factor FB parents_immi Gparents_immi
(obs=3,922)

Factor analysis/correlation                      Number of obs    =      3,922
    Method: principal factors                    Retained factors =          2
    Rotation: (unrotated)                        Number of params =          3

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      1.56048      1.54763            1.1528       1.1528
        Factor2  |      0.01285      0.23253            0.0095       1.1623
        Factor3  |     -0.21968            .           -0.1623       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(3)  = 3913.16 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
              FB |   0.6193    0.0844 |      0.6093  
    parents_immi |   0.8324    0.0004 |      0.3071  
    Gparents_i~i |   0.6957   -0.0756 |      0.5103  
    -------------------------------------------------

. predict immiF
(option regression assumed; regression scoring)

Scoring coefficients (method = regression)

    ----------------------------------
        Variable |  Factor1   Factor2 
    -------------+--------------------
              FB |  0.20292   0.12630 
    parents_immi |  0.53890   0.01296 
    Gparents_i~i |  0.26368  -0.13167 
    ----------------------------------


. 
. 
. 
. 
end of do-file

. 
. **-- flag missing values 
. gen miss_child = 1 if child2 == . 
(4,174 missing values generated)

. replace miss_child = 0 if child2 != . 
(4,174 real changes made)

. replace child2 = 99 if child2 == . 
(6 real changes made)

. 
. gen inc_miss = 0

. replace inc_miss = 1 if ppincimp >= .  
(31 real changes made)

. replace ppincimp = 99 if ppincimp >= .  
(31 real changes made)

. 
. tab method, gen(meth)

                Survey method treatment |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                 Likert |      1,392       33.30       33.30
                                Likert+ |      1,391       33.28       66.58
                                   QVSR |      1,397       33.42      100.00
----------------------------------------+-----------------------------------
                                  Total |      4,180      100.00

. tab xparty7, gen(prty)

                   Party ID |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
          Strong Republican |        704       16.97       16.97
      Not Strong Republican |        510       12.29       29.26
           Leans Republican |        723       17.43       46.69
Undecided/Independent/Other |         71        1.71       48.40
             Leans Democrat |        685       16.51       64.91
        Not Strong Democrat |        524       12.63       77.54
            Strong Democrat |        932       22.46      100.00
----------------------------+-----------------------------------
                      Total |      4,149      100.00

. recode ppeducat 1=2
(171 changes made to ppeducat)

. tab ppeducat, gen(educ)

    Education (Categorical) |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
                High school |      1,230       29.65       29.65
               Some college |      1,279       30.83       60.47
Bachelor's degree or higher |      1,640       39.53      100.00
----------------------------+-----------------------------------
                      Total |      4,149      100.00

. recode gunOa (2 3 = 0) (4 = 1)
(4,149 changes made to gunOa)

. recode ppethm (5 =3)
(129 changes made to ppethm)

. tab ppethm, gen(ethnic)

      Race / Ethnicity |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
   White, Non-Hispanic |      3,128       75.39       75.39
   Black, Non-Hispanic |        367        8.85       84.24
   Other, Non-Hispanic |        285        6.87       91.11
              Hispanic |        369        8.89      100.00
-----------------------+-----------------------------------
                 Total |      4,149      100.00

. recode minWa (1=0) (2 3 = 1)
(2,544 changes made to minWa)

. replace minWa = 99 if minWa == . 
(1,636 real changes made)

. tab minWa, gen(MW)

      minWa |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,476       35.31       35.31
          1 |      1,068       25.55       60.86
         99 |      1,636       39.14      100.00
------------+-----------------------------------
      Total |      4,180      100.00

. replace Nevangelical = 99 if Nevangelical == . 
(1,272 real changes made)

. tab Nevangelical, gen(BA)

  RECODE of |
evangelical |
 (RECODE of |
  xppp20071 |
     (Q26A: |
  Would you |
   describe |
yourself as |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,092       26.12       26.12
          1 |      1,816       43.44       69.57
         99 |      1,272       30.43      100.00
------------+-----------------------------------
      Total |      4,180      100.00

. replace evangelical = (evangelical * -1) +3
(2,908 real changes made)

. 
. 
. 
. **** flag IDs that made it into the final sample for the gun donation analysis 
. preserve 

. use "$pathout/dataset_final.dta", replace

. 
. gen comp_exp_w1 = 0

. replace comp_exp_w1 = 1 if votes_AAw1 < . & votes_gunw1 < . & votes_wallw1 < . & votes_paidLw1 < . & ///
> votes_genderw1 < . & votes_gayw1 < . & votes_minWw1 < . & votes_abortionw1 < . & votes_deficitw1 < . & ///
> votes_envirow1 < . & sex < . 
(3,917 real changes made)

. 
. keep if comp_exp_w1 == 1 
(47 observations deleted)

. 
. quietly bys id : gen dupF = cond(_N==1,0,_n)

. 
. gen drop_out = 0 

. replace drop_out = 99 if dupF > 1 
(26 real changes made)

. keep id drop_out comp_exp_w1 method

. keep if drop_out < . 
(0 observations deleted)

. 
. 
. save "$pathtemp/ids_dataset_final.dta", replace
(file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp/ids_dataset_final.dta not found)
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp/ids_dataset_final.dta saved

. restore 

. 
. *** flag dup in the baseline data
. quietly bys id : gen dupO = cond(_N==1,0,_n)

. 
. 
. merge m:1 id method using "$pathtemp/ids_dataset_final.dta"
(label method_lbl already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                           232
        from master                       232  (_merge==1)
        from using                          0  (_merge==2)

    Matched                             3,948  (_merge==3)
    -----------------------------------------

. 
. *** obs that dropped out 
. replace drop_out = 1 if _merge == 1
(232 real changes made)

. ** obs that duplicate 
. replace drop_out = 1 if drop_out == 99 
(28 real changes made)

. 
. 
. tab drop_out method , col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

           |     Survey method treatment
  drop_out |    Likert    Likert+       QVSR |     Total
-----------+---------------------------------+----------
         0 |     1,336      1,331      1,253 |     3,920 
           |     95.98      95.69      89.69 |     93.78 
-----------+---------------------------------+----------
         1 |        56         60        144 |       260 
           |      4.02       4.31      10.31 |      6.22 
-----------+---------------------------------+----------
     Total |     1,392      1,391      1,397 |     4,180 
           |    100.00     100.00     100.00 |    100.00 

. 
. 
. 
. ** gen table 
. 
. gen Likert = drop_out

. gen Likert_plus = drop_out

. gen QVSR = drop_out

. 
. reg Likert  c.xparty7 c.xideo educ2 educ3 c.ppage  sex  ethnic2 ethnic3 ethnic4  MW2 MW3  c.child2  i.gunOa ///
> parents_immi  i.gay  BA2 BA3 c.ppincimp if method == 1

      Source |       SS           df       MS      Number of obs   =     1,377
-------------+----------------------------------   F(18, 1358)     =      1.32
       Model |   .71434024        18  .039685569   Prob > F        =    0.1676
    Residual |  40.9428856     1,358    .0301494   R-squared       =    0.0171
-------------+----------------------------------   Adj R-squared   =    0.0041
       Total |  41.6572259     1,376  .030274147   Root MSE        =    .17364

------------------------------------------------------------------------------
      Likert | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     xparty7 |   .0041575   .0031429     1.32   0.186     -.002008     .010323
       xideo |  -.0004269   .0042854    -0.10   0.921    -.0088337    .0079799
       educ2 |   -.003837   .0123777    -0.31   0.757    -.0281186    .0204445
       educ3 |  -.0006464   .0128836    -0.05   0.960    -.0259203    .0246274
       ppage |   .0003397   .0003335     1.02   0.309    -.0003145    .0009939
         sex |   .0034256   .0095565     0.36   0.720    -.0153214    .0221727
     ethnic2 |   .0418681   .0188039     2.23   0.026     .0049804    .0787559
     ethnic3 |  -.0012269   .0199188    -0.06   0.951    -.0403018     .037848
     ethnic4 |  -.0054621   .0180605    -0.30   0.762    -.0408916    .0299675
         MW2 |  -.0004302   .0132411    -0.03   0.974    -.0264055     .025545
         MW3 |   .0051314   .0126555     0.41   0.685     -.019695    .0299578
      child2 |  -.0007063   .0017533    -0.40   0.687    -.0041457    .0027332
     1.gunOa |   .0096665   .0100337     0.96   0.336    -.0100167    .0293497
parents_immi |   .0063837   .0086153     0.74   0.459    -.0105169    .0232843
       1.gay |   .0029015   .0188535     0.15   0.878    -.0340837    .0398866
         BA2 |  -.0025846   .0121828    -0.21   0.832    -.0264837    .0213146
         BA3 |   .0132841   .0141626     0.94   0.348    -.0144987     .041067
    ppincimp |  -.0010186   .0012421    -0.82   0.412    -.0034554    .0014181
       _cons |  -.0133296   .0427036    -0.31   0.755    -.0971019    .0704426
------------------------------------------------------------------------------

. estimates store dropw1

. 
. reg Likert_plus c.xparty7 c.xideo educ2 educ3 c.ppage  sex  ethnic2 ethnic3 ethnic4  MW2 MW3  c.child2  i.gunOa ///
> parents_immi  i.gay   BA2 BA3 c.ppincimp if method == 2

      Source |       SS           df       MS      Number of obs   =     1,378
-------------+----------------------------------   F(18, 1359)     =      1.24
       Model |  .822078524        18  .045671029   Prob > F        =    0.2219
    Residual |  50.1394599     1,359  .036894378   R-squared       =    0.0161
-------------+----------------------------------   Adj R-squared   =    0.0031
       Total |  50.9615385     1,377  .037009106   Root MSE        =    .19208

------------------------------------------------------------------------------
 Likert_plus | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     xparty7 |   .0021807   .0034566     0.63   0.528    -.0046001    .0089616
       xideo |  -.0053542    .004701    -1.14   0.255    -.0145761    .0038678
       educ2 |   .0092191   .0138676     0.66   0.506    -.0179851    .0364233
       educ3 |   .0069305   .0146682     0.47   0.637    -.0218443    .0357054
       ppage |    .000966   .0003755     2.57   0.010     .0002294    .0017027
         sex |   .0115703   .0107347     1.08   0.281    -.0094881    .0326286
     ethnic2 |  -.0092627   .0195013    -0.47   0.635    -.0475186    .0289932
     ethnic3 |   .0217221   .0218549     0.99   0.320    -.0211508    .0645951
     ethnic4 |   .0190037   .0208949     0.91   0.363    -.0219861    .0599936
         MW2 |   .0034143   .0147102     0.23   0.816    -.0254428    .0322713
         MW3 |  -.0210047   .0139372    -1.51   0.132    -.0483454    .0063359
      child2 |  -.0001096   .0019575    -0.06   0.955    -.0039497    .0037305
     1.gunOa |   -.015767    .011318    -1.39   0.164    -.0379696    .0064357
parents_immi |  -.0031861   .0096807    -0.33   0.742    -.0221767    .0158046
       1.gay |  -.0214257   .0231817    -0.92   0.356    -.0669016    .0240501
         BA2 |   .0023108   .0132591     0.17   0.862    -.0236997    .0283212
         BA3 |  -.0069068   .0157408    -0.44   0.661    -.0377856    .0239721
    ppincimp |  -.0030704    .001433    -2.14   0.032    -.0058816   -.0002593
       _cons |   .0396122   .0489605     0.81   0.419    -.0564342    .1356587
------------------------------------------------------------------------------

. estimates store dropw2

. 
. reg QVSR  c.xparty7 c.xideo educ2 educ3 c.ppage  sex  ethnic2 ethnic3 ethnic4  MW2 MW3  c.child2  i.gunOa ///
> parents_immi i.gay  BA2 BA3 c.ppincimp if method == 3 

      Source |       SS           df       MS      Number of obs   =     1,382
-------------+----------------------------------   F(18, 1363)     =      1.13
       Model |  1.75441845        18  .097467692   Prob > F        =    0.3164
    Residual |  117.637767     1,363  .086307973   R-squared       =    0.0147
-------------+----------------------------------   Adj R-squared   =    0.0017
       Total |  119.392185     1,381  .086453429   Root MSE        =    .29378

------------------------------------------------------------------------------
        QVSR | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     xparty7 |   .0104756   .0053549     1.96   0.051    -.0000292    .0209804
       xideo |   .0142781   .0073235     1.95   0.051    -.0000884    .0286446
       educ2 |  -.0285841    .021024    -1.36   0.174     -.069827    .0126588
       educ3 |  -.0180599   .0220463    -0.82   0.413    -.0613082    .0251885
       ppage |   .0008811   .0006111     1.44   0.150    -.0003178      .00208
         sex |   .0183126    .016228     1.13   0.259     -.013522    .0501472
     ethnic2 |   .0035778   .0306181     0.12   0.907     -.056486    .0636415
     ethnic3 |  -.0213199   .0346525    -0.62   0.538    -.0892978    .0466581
     ethnic4 |  -.0523363   .0317379    -1.65   0.099    -.1145967    .0099241
         MW2 |   -.009804   .0227197    -0.43   0.666    -.0543734    .0347655
         MW3 |   .0099854   .0219899     0.45   0.650    -.0331523    .0531231
      child2 |   .0006386   .0029956     0.21   0.831    -.0052379    .0065152
     1.gunOa |   .0011326   .0171625     0.07   0.947    -.0325352    .0348003
parents_immi |   .0187175   .0154506     1.21   0.226    -.0115921     .049027
       1.gay |   .0120343   .0324786     0.37   0.711    -.0516791    .0757477
         BA2 |   .0197208   .0205794     0.96   0.338      -.02065    .0600916
         BA3 |   .0239083   .0235199     1.02   0.310    -.0222307    .0700474
    ppincimp |  -.0009615   .0021395    -0.45   0.653    -.0051587    .0032356
       _cons |  -.0896725   .0754123    -1.19   0.235    -.2376093    .0582644
------------------------------------------------------------------------------

. estimates store dropw3

. 
. cd "$pathtab"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/tab

. 
. 
. esttab dropw1 dropw2 dropw3 using attrition.tex, replace ///
> style(tex) cells(b(star fmt(3)) se(par(( )) fmt(3))) starlevels(* .05 ** .01 *** .001) ///
> stats(N, fmt(0)) ///
> drop(0.gunOa 0.gay) ///
> varlabels(_cons cons ///
> xparty7 Partisanship ///
> xideo Ideology ///
> educ2 Some_College ///
> educ3 BA ///
> ppage Age ///
> sex Gender ///
> ethnic2 Black ///
> ethnic3 Other ///
> ethnic4 Hispanic ///
> MW2 Exp_minW ///
> MW3 MissV_minW ///
> child2 Paid_leave_exp ///
> 1.gunOa Gun_exp ///
> parents_immi Immi_exp ///
> 1.gay Sexual_orientation ///
> BA2 Born_again ///
> BA3 MissV_BA ///
> ppincimp income)
(output written to attrition.tex)

. 
. 
. ***********************************************************
. * OUTPUT TAB C5                                                           *
. * PLEASE RUN CORRESPONDING LATEX FILE AVAILABLE IN "TAB" *
. ***********************************************************
. 
. 
. 
. *----- 
. *-------------------------------
. *---  C. Survey design. Participation in wave 2 using all observable covariates (Table C6)
. 
. 
. 
. use "$pathout/dataset_final.dta", replace

. 
. *** drop people not eligible
. gen comp_bev_w1 = 0

. replace comp_bev_w1 = 1 if votes_AAw1 < . & votes_gunw1 < . & votes_wallw1 < . & votes_paidLw1 < . & ///
> votes_genderw1 < . & votes_gayw1 < . & votes_minWw1 < . & votes_abortionw1 < . & votes_deficitw1 < . & ///
> votes_envirow1 < . & don_C_gun < . 
(3,679 real changes made)

. keep if comp_bev_w1 == 1 
(285 observations deleted)

. 
. 
. 
. *** identify folks who consented in wave 2
. preserve 

. use "$pathdata/part1_wave2_qualtrics.dta", replace

. 
. rename mno id

. rename state2 session

. 
. rename xtreat method

. drop if method == 7
(292 observations deleted)

. 
. drop if id == .
(2 observations deleted)

. drop if ICN_Q2 == "No"
(26 observations deleted)

. 
. 
. **-- will be matched to eligible using id , so need to address doublons
. quietly bys id  :  gen dup_p1 = cond(_N==1,0,_n)

. tab dup_p1

     dup_p1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,580       90.70       90.70
          1 |         74        4.25       94.95
          2 |         74        4.25       99.20
          3 |         10        0.57       99.77
          4 |          1        0.06       99.83
          5 |          1        0.06       99.89
          6 |          1        0.06       99.94
          7 |          1        0.06      100.00
------------+-----------------------------------
      Total |      1,742      100.00

. **--- 74 doublons
. 
. **- order in which showed up in survey wave 2
. sort id StartDate 

. ** generate the order variable
. quietly by id   : gen n_p1 = _n

. 
. ** keep the first one 
. keep if dup_p1 == 0 | dup_p1 > 0 & n_p1 == 1 
(88 observations deleted)

. 
. drop dup_p1 n_p1 

. 
. gen wave2_p = 1

. keep id wave2_p

. 
. rename id id_w2

. *** merge wave 2 with wave 1 IDs
. 
. merge 1:1 id_w2 using "$pathdata/mno_id_W1W2.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                           334
        from master                         2  (_merge==1)
        from using                        332  (_merge==2)

    Matched                             1,652  (_merge==3)
    -----------------------------------------

. keep if _merge == 3 
(334 observations deleted)

. drop _merge id_w2

. rename id_w1 id 

. 
. save "$pathtemp/ids_wave2.dta", replace
(file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp/ids_wave2.dta not found)
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp/ids_wave2.dta saved

. restore 

. 
. 
. **** merge 
. merge m:1 id using "$pathtemp/ids_wave2.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                         2,239
        from master                     2,132  (_merge==1)
        from using                        107  (_merge==2)

    Matched                             1,547  (_merge==3)
    -----------------------------------------

. tab wave2_p _merge, miss

           |    Matching result from merge
   wave2_p | Master on  Using onl  Matched ( |     Total
-----------+---------------------------------+----------
         1 |         0        107      1,547 |     1,654 
         . |     2,132          0          0 |     2,132 
-----------+---------------------------------+----------
     Total |     2,132        107      1,547 |     3,786 

. replace wave2_p = 0 if _merge == 1 & wave2_p == . 
(2,132 real changes made)

. 
. 
. tab method, gen(meth)

                Survey method treatment |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                 Likert |      1,257       34.17       34.17
                                Likert+ |      1,259       34.22       68.39
                                   QVSR |      1,163       31.61      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,679      100.00

. recode ppeducat 1=2
(142 changes made to ppeducat)

. tab ppeducat, gen(educ)

    Education (Categorical) |      Freq.     Percent        Cum.
----------------------------+-----------------------------------
                High school |      1,062       29.02       29.02
               Some college |      1,126       30.77       59.80
Bachelor's degree or higher |      1,471       40.20      100.00
----------------------------+-----------------------------------
                      Total |      3,659      100.00

. recode gunOa (2 3 = 0) (4 = 1)
(3,659 changes made to gunOa)

. recode ppethm (5 =3)
(112 changes made to ppethm)

. tab ppethm, gen(ethnic)

      Race / Ethnicity |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
   White, Non-Hispanic |      2,767       75.62       75.62
   Black, Non-Hispanic |        321        8.77       84.39
   Other, Non-Hispanic |        250        6.83       91.23
              Hispanic |        321        8.77      100.00
-----------------------+-----------------------------------
                 Total |      3,659      100.00

. recode minWa (1=0) (2 3 = 1)
(2,286 changes made to minWa)

. replace minWa = 99 if minWa == . 
(1,500 real changes made)

. tab minWa, gen(MW)

      minWa |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,330       35.13       35.13
          1 |        956       25.25       60.38
         99 |      1,500       39.62      100.00
------------+-----------------------------------
      Total |      3,786      100.00

. replace Nevangelical = 99 if Nevangelical == . 
(1,234 real changes made)

. tab Nevangelical, gen(BA)

  RECODE of |
evangelical |
 (RECODE of |
  xppp20071 |
     (Q26A: |
  Would you |
   describe |
yourself as |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        957       25.28       25.28
          1 |      1,595       42.13       67.41
         99 |      1,234       32.59      100.00
------------+-----------------------------------
      Total |      3,786      100.00

. replace evangelical = (evangelical * -1) +3
(2,552 real changes made)

. 
. gen Likert = wave2_p

. gen Likert_plus = wave2_p

. gen QVSR = wave2_p

. 
. reg Likert c.xparty7  c.xideo educ2 educ3 c.ppage  sex  ethnic2 ethnic3 ethnic4  ///
>  MW2 MW3  c.child2  i.gunOa ///
> parents_immi  i.gay  BA2 BA3 c.ppincimp if method == 1

      Source |       SS           df       MS      Number of obs   =     1,245
-------------+----------------------------------   F(18, 1226)     =      1.75
       Model |  7.50049657        18  .416694254   Prob > F        =    0.0270
    Residual |  292.470588     1,226   .23855676   R-squared       =    0.0250
-------------+----------------------------------   Adj R-squared   =    0.0107
       Total |  299.971084     1,244  .241134312   Root MSE        =    .48842

------------------------------------------------------------------------------
      Likert | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     xparty7 |   .0034279   .0093215     0.37   0.713    -.0148601    .0217158
       xideo |  -.0103478    .012677    -0.82   0.415    -.0352187    .0145231
       educ2 |   .0399519   .0367913     1.09   0.278     -.032229    .1121327
       educ3 |  -.0207504   .0380469    -0.55   0.586    -.0953947     .053894
       ppage |   .0008911   .0013304     0.67   0.503    -.0017191    .0035013
         sex |  -.0253523   .0282671    -0.90   0.370    -.0808095     .030105
     ethnic2 |  -.1137343   .0564894    -2.01   0.044    -.2245609   -.0029076
     ethnic3 |  -.1365603    .058449    -2.34   0.020    -.2512314   -.0218891
     ethnic4 |  -.0643483   .0543779    -1.18   0.237    -.1710324    .0423357
         MW2 |  -.0009298    .038883    -0.02   0.981    -.0772143    .0753548
         MW3 |   .0288432   .0373581     0.77   0.440    -.0444498    .1021361
      child2 |  -.0020815   .0256483    -0.08   0.935    -.0524011     .048238
     1.gunOa |   .0967988   .0295136     3.28   0.001      .038896    .1547016
parents_immi |   .0064888   .0261665     0.25   0.804    -.0448473     .057825
       1.gay |  -.0349596   .0567434    -0.62   0.538    -.1462845    .0763654
         BA2 |  -.0252257   .0362489    -0.70   0.487    -.0963424    .0458909
         BA3 |  -.0006861   .0419962    -0.02   0.987    -.0830784    .0817062
    ppincimp |   .0072126   .0036786     1.96   0.050    -4.44e-06    .0144297
       _cons |    .288077   .1519101     1.90   0.058    -.0099555    .5861095
------------------------------------------------------------------------------

. estimates store bet1

. 
. reg Likert_plus c.xparty7  c.xideo educ2 educ3 c.ppage  sex  ethnic2 ethnic3 ethnic4  ///
>  MW2 MW3  c.child2  i.gunOa ///
> parents_immi  i.gay  BA2 BA3 c.ppincimp if method == 2

      Source |       SS           df       MS      Number of obs   =     1,249
-------------+----------------------------------   F(18, 1230)     =      1.68
       Model |  7.27467819        18  .404148788   Prob > F        =    0.0370
    Residual |  296.063993     1,230  .240702433   R-squared       =    0.0240
-------------+----------------------------------   Adj R-squared   =    0.0097
       Total |  303.338671     1,248  .243059832   Root MSE        =    .49061

------------------------------------------------------------------------------
 Likert_plus | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     xparty7 |     .00128   .0092368     0.14   0.890    -.0168416    .0194016
       xideo |  -.0049014   .0125118    -0.39   0.695    -.0294482    .0196454
       educ2 |   .0068501   .0372755     0.18   0.854    -.0662806    .0799808
       educ3 |   .0501889     .03968     1.26   0.206    -.0276591    .1280369
       ppage |    .002716   .0013715     1.98   0.048     .0000252    .0054068
         sex |  -.0579112   .0288024    -2.01   0.045    -.1144185   -.0014039
     ethnic2 |  -.0823005   .0523641    -1.57   0.116    -.1850334    .0204324
     ethnic3 |  -.0721407    .060323    -1.20   0.232     -.190488    .0462066
     ethnic4 |   .0395146   .0568543     0.70   0.487    -.0720277    .1510568
         MW2 |   .0112673   .0393352     0.29   0.775    -.0659043    .0884388
         MW3 |   .0493039   .0376972     1.31   0.191     -.024654    .1232617
      child2 |   .0224931   .0259391     0.87   0.386    -.0283966    .0733828
     1.gunOa |  -.0200086   .0305615    -0.65   0.513     -.079967    .0399498
parents_immi |   .0317632   .0263198     1.21   0.228    -.0198735    .0833999
       1.gay |   .0465766   .0610924     0.76   0.446    -.0732803    .1664335
         BA2 |  -.0053995   .0360073    -0.15   0.881     -.076042     .065243
         BA3 |  -.0489789   .0427793    -1.14   0.252    -.1329074    .0349495
    ppincimp |    .002103    .003923     0.54   0.592    -.0055935    .0097994
       _cons |   .2651208   .1559533     1.70   0.089    -.0408431    .5710847
------------------------------------------------------------------------------

. estimates store bet2

. 
. reg QVSR c.xparty7  c.xideo educ2 educ3 c.ppage  sex  ethnic2 ethnic3 ethnic4  ///
>  MW2 MW3  c.child2  i.gunOa ///
> parents_immi  i.gay  BA2 BA3 c.ppincimp if method == 3

      Source |       SS           df       MS      Number of obs   =     1,155
-------------+----------------------------------   F(18, 1136)     =      1.78
       Model |  7.83521478        18   .43528971   Prob > F        =    0.0233
    Residual |  278.052231     1,136  .244764288   R-squared       =    0.0274
-------------+----------------------------------   Adj R-squared   =    0.0120
       Total |  285.887446     1,154  .247736088   Root MSE        =    .49474

------------------------------------------------------------------------------
        QVSR | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     xparty7 |  -.0168638   .0098444    -1.71   0.087     -.036179    .0024514
       xideo |  -.0225562   .0134849    -1.67   0.095    -.0490143    .0039019
       educ2 |   .0555581   .0387789     1.43   0.152    -.0205283    .1316445
       educ3 |   .0473302   .0409776     1.16   0.248      -.03307    .1277305
       ppage |    .002604   .0015247     1.71   0.088    -.0003875    .0055955
         sex |  -.0261274   .0298124    -0.88   0.381     -.084621    .0323662
     ethnic2 |   .0382963     .05578     0.69   0.493    -.0711471    .1477398
     ethnic3 |   .0435608   .0638417     0.68   0.495    -.0817001    .1688218
     ethnic4 |   -.084487   .0573634    -1.47   0.141    -.1970371    .0280632
         MW2 |  -.0040471   .0415248    -0.10   0.922    -.0855211    .0774268
         MW3 |   .0496746   .0412467     1.20   0.229    -.0312535    .1306028
      child2 |   .0060932   .0273909     0.22   0.824    -.0476493    .0598357
     1.gunOa |   .0371719   .0316175     1.18   0.240    -.0248633    .0992071
parents_immi |  -.0061576   .0284102    -0.22   0.828       -.0619    .0495849
       1.gay |   .0454811   .0596278     0.76   0.446    -.0715119    .1624741
         BA2 |  -.0278462   .0378606    -0.74   0.462    -.1021308    .0464383
         BA3 |  -.0739215   .0431412    -1.71   0.087    -.1585668    .0107239
    ppincimp |   .0036024   .0039744     0.91   0.365    -.0041957    .0114004
       _cons |   .4210173   .1647931     2.55   0.011     .0976843    .7443503
------------------------------------------------------------------------------

. estimates store bet3

. 
. cd "$pathtab"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/tab

. 
. esttab bet1 bet2 bet3  using attrition_betweenv1.tex, replace ///
> style(tex) cells(b(star fmt(3)) se(par(( )) fmt(3))) starlevels(* .05 ** .01 *** .001) ///
> stats(N, fmt(0)) ///
> drop(0.gunOa 0.gay) ///
> varlabels(_cons cons ///
> xparty7 Partisanship ///
> xideo Ideology ///
> educ2 Some_College ///
> educ3 BA ///
> ppage Age ///
> sex Gender ///
> ethnic2 Black ///
> ethnic3 Other ///
> ethnic4 Hispanic ///
> MW2 Exp_minW ///
> MW3 MissV_minW ///
> child2 Paid_leave_exp ///
> 1.gunOa Gun_exp ///
> parents_immi Immi_exp ///
> 1.gay Sexual_orientation ///
> BA2 Born_again ///
> BA3 MissV_BA ///
> ppincimp income)
(output written to attrition_betweenv1.tex)

. 
. *reg wave2_p i.ethnic2##i.method i.ethnic3##i.method i.ethnic4##i.method i.gunOa##i.method
. 
. **********************************************************
. * OUTPUT TAB C6                                                          *
. * PLEASE RUN CORRESPONDING LATEX FILE AVAILABLE IN "TAB" *
. **********************************************************
. 
. 
. 
. *----- 
. *-------------------------------
. *---  C. Survey design. Participation in wave 2 using w1 preferences (Table C7)
. 
. 
. reg Likert c.don_C_gun c.don_C_wall_in abs_votes_gunw1 abs_votes_wallw1 ///
> abs_votes_minWw1 abs_votes_abortionw1 ///
> if method == 1

      Source |       SS           df       MS      Number of obs   =     1,257
-------------+----------------------------------   F(6, 1250)      =      1.04
       Model |  1.49407807         6  .249013012   Prob > F        =    0.4000
    Residual |  300.424776     1,250  .240339821   R-squared       =    0.0049
-------------+----------------------------------   Adj R-squared   =    0.0002
       Total |  301.918854     1,256  .240381254   Root MSE        =    .49024

--------------------------------------------------------------------------------------
              Likert | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
           don_C_gun |   .0001271   .0004256     0.30   0.765    -.0007079     .000962
       don_C_wall_in |   .0000899     .00049     0.18   0.855    -.0008715    .0010512
     abs_votes_gunw1 |  -.0069055   .0134605    -0.51   0.608    -.0333131    .0195021
    abs_votes_wallw1 |   .0251614   .0128721     1.95   0.051    -.0000919    .0504147
    abs_votes_minWw1 |  -.0039736   .0128796    -0.31   0.758    -.0292416    .0212943
abs_votes_abortionw1 |   .0139804    .012499     1.12   0.264     -.010541    .0385018
               _cons |   .3345225   .0490979     6.81   0.000     .2381991    .4308459
--------------------------------------------------------------------------------------

. estimates store bet1a

. 
. reg Likert_plus c.don_C_gun c.don_C_wall_in abs_votes_gunw1 abs_votes_wallw1 ///
> abs_votes_minWw1 abs_votes_abortionw1 ///
> if method == 2

      Source |       SS           df       MS      Number of obs   =     1,259
-------------+----------------------------------   F(6, 1252)      =      0.47
       Model |  .686690274         6  .114448379   Prob > F        =    0.8309
    Residual |  304.884398     1,252   .24351789   R-squared       =    0.0022
-------------+----------------------------------   Adj R-squared   =   -0.0025
       Total |  305.571088     1,258  .242902296   Root MSE        =    .49348

--------------------------------------------------------------------------------------
         Likert_plus | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
           don_C_gun |  -.0003015   .0004044    -0.75   0.456    -.0010948    .0004918
       don_C_wall_in |   .0002734   .0004693     0.58   0.560    -.0006473    .0011941
     abs_votes_gunw1 |  -.0081028    .009325    -0.87   0.385    -.0263971    .0101916
    abs_votes_wallw1 |   .0037023   .0086252     0.43   0.668    -.0132191    .0206236
    abs_votes_minWw1 |  -.0024549   .0085537    -0.29   0.774     -.019236    .0143262
abs_votes_abortionw1 |   .0088173    .008201     1.08   0.283    -.0072718    .0249064
               _cons |   .4133897   .0444004     9.31   0.000     .3262822    .5004971
--------------------------------------------------------------------------------------

. estimates store bet2a

. 
. reg QVSR c.don_C_gun c.don_C_wall_in abs_votes_gunw1 abs_votes_wallw1 ///
> abs_votes_minWw1 abs_votes_abortionw1 ///
> if method == 3

      Source |       SS           df       MS      Number of obs   =     1,163
-------------+----------------------------------   F(6, 1156)      =      1.86
       Model |  2.74840563         6  .458067605   Prob > F        =    0.0848
    Residual |  284.854346     1,156  .246413794   R-squared       =    0.0096
-------------+----------------------------------   Adj R-squared   =    0.0044
       Total |  287.602752     1,162  .247506671   Root MSE        =     .4964

--------------------------------------------------------------------------------------
                QVSR | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
           don_C_gun |   .0002188   .0004569     0.48   0.632    -.0006776    .0011153
       don_C_wall_in |  -3.74e-06   .0005211    -0.01   0.994    -.0010262    .0010187
     abs_votes_gunw1 |   .0039253   .0092495     0.42   0.671    -.0142225     .022073
    abs_votes_wallw1 |   .0217093   .0089586     2.42   0.016     .0041324    .0392862
    abs_votes_minWw1 |  -.0182752   .0106856    -1.71   0.087    -.0392405    .0026901
abs_votes_abortionw1 |   .0054409   .0088769     0.61   0.540    -.0119757    .0228575
               _cons |   .3964147   .0530917     7.47   0.000     .2922477    .5005816
--------------------------------------------------------------------------------------

. estimates store bet3a

. 
. 
. esttab bet1a bet2a bet3a  using attrition_betweenv2.tex, replace ///
> style(tex) cells(b(star fmt(3)) se(par(( )) fmt(3))) starlevels(* .05 ** .01 *** .001) ///
> stats(N, fmt(0)) ///
> title(Predicting Participation in Wave 2 Using Wave 1 Donations and Opinions \label{attritionv2}) ///
> varlabels(_cons cons ///
> don_C_gun Donation_gun ////
> don_C_wall_in Donation_immi ///
> abs_votes_gunw1 Opinion_gun_abs  ///
> abs_votes_wallw1 Opinion_wall_abs ///
> abs_votes_minWw1 Opinion_minW_abs ///
> abs_votes_abortionw1 Opinion_abortion_abs )
(output written to attrition_betweenv2.tex)

. 
. *reg wave2_p c.abs_votes_wallw1##i.method 
. 
. 
. **********************************************************
. * OUTPUT TAB C7                                                          *
. * PLEASE RUN CORRESPONDING LATEX FILE AVAILABLE IN "TAB" *
. **********************************************************
. 
. 
. *----- 
. *-------------------------------
. *---  D. Using Wave 2 preferences (Fig D1)
. 
. use "$pathout/dataset_final.dta", replace

. 
. matrix EV = J(6,6,0)

. matrix colnames EV = "Likert" " " "Likert +" " " "QVSR" " " 

. matrix rownames EV = "Gun-related Donations" ///
> "Immigration-related Donations" ///
> "Minimum Wage-related Writing" /// 
> "Abortion-related Writing" ///
> "Punish abs" ///
> "Punish proportion"

. 
. 
. regress don_C_gunST c.votes_gunw2LPP3N##i.method i.block

      Source |       SS           df       MS      Number of obs   =     1,502
-------------+----------------------------------   F(45, 1456)     =      9.62
       Model |  347.657002        45  7.72571116   Prob > F        =    0.0000
    Residual |  1168.73359     1,456  .802701641   R-squared       =    0.2293
-------------+----------------------------------   Adj R-squared   =    0.2054
       Total |  1516.39059     1,501  1.01025356   Root MSE        =    .89594

-------------------------------------------------------------------------------------------
              don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
         votes_gunw2LPP3N |   .8952889   .1180187     7.59   0.000      .663784    1.126794
                          |
                   method |
                 Likert+  |  -.0236825   .1209384    -0.20   0.845    -.2609145    .2135496
                    QVSR  |  -.3286807   .1460046    -2.25   0.025    -.6150826   -.0422788
                          |
method#c.votes_gunw2LPP3N |
                 Likert+  |   .0708339    .162452     0.44   0.663    -.2478311    .3894989
                    QVSR  |   .7069516   .2179723     3.24   0.001     .2793783    1.134525
                          |
                    block |
                    1120  |  -.1105926   .3697736    -0.30   0.765    -.8359385    .6147533
                    1130  |  -.3609346   .4517252    -0.80   0.424    -1.247036    .5251671
                    1210  |  -.2880554   .3341701    -0.86   0.389    -.9435617    .3674509
                    1221  |  -.3428332   .3636324    -0.94   0.346    -1.056133    .3704661
                    1222  |  -.1057912   .3332226    -0.32   0.751    -.7594388    .5478565
                    1223  |  -.5042147   .3305962    -1.53   0.127     -1.15271     .144281
                    1231  |  -.3706601   .4728313    -0.78   0.433    -1.298163    .5568432
                    1232  |   .1218534    .383508     0.32   0.751    -.6304339    .8741406
                    1233  |  -.2581754   .3372598    -0.77   0.444    -.9197424    .4033915
                    1311  |  -.5276249   .4037832    -1.31   0.192    -1.319684     .264434
                    1312  |  -.7203448   .3602012    -2.00   0.046    -1.426914    -.013776
                    1313  |  -.4309381   .3354145    -1.28   0.199    -1.088885    .2270091
                    1321  |  -.3120567    .307253    -1.02   0.310    -.9147626    .2906492
                    1322  |  -.2670651   .3155688    -0.85   0.398    -.8860831     .351953
                    1323  |  -.4796397   .3137455    -1.53   0.127    -1.095081    .1358018
                    1331  |  -.2719047   .3602302    -0.75   0.450    -.9785304     .434721
                    1332  |  -.4536468   .3571945    -1.27   0.204    -1.154318    .2470239
                    1333  |  -.3114748   .3547368    -0.88   0.380    -1.007325    .3843751
                    2010  |   .1005638   .4519303     0.22   0.824    -.7859404    .9870679
                    2020  |   -.101283   .5005436    -0.20   0.840    -1.083147    .8805806
                    2030  |   .0428956   .3831396     0.11   0.911    -.7086691    .7944603
                    3115  |   .1124898   .3166558     0.36   0.722    -.5086605      .73364
                    3116  |   -.027173   .3144485    -0.09   0.931    -.6439935    .5896475
                    3117  |   .0570614   .3050065     0.19   0.852    -.5412378    .6553606
                    3120  |  -.2051647   .4028311    -0.51   0.611    -.9953561    .5850267
                    3135  |  -.5516407   .3739337    -1.48   0.140    -1.285147    .1818657
                    3136  |  -.2182066   .3956331    -0.55   0.581    -.9942783    .5578652
                    3137  |   .1768113   .3505546     0.50   0.614    -.5108348    .8644574
                    3215  |   .0698592   .3190377     0.22   0.827    -.5559635    .6956819
                    3216  |   .1120209   .3274372     0.34   0.732    -.5302781      .75432
                    3217  |   .1193904   .3223677     0.37   0.711    -.5129644    .7517453
                    3220  |  -.0652369    .369658    -0.18   0.860    -.7903561    .6598822
                    3235  |  -.2294159   .3300642    -0.70   0.487     -.876868    .4180362
                    3236  |  -.1900821   .3663008    -0.52   0.604    -.9086157    .5284515
                    3237  |  -.4601199   .4224972    -1.09   0.276    -1.288888    .3686483
                    3315  |   .1792187   .3694805     0.49   0.628    -.5455522    .9039897
                    3316  |     .39293    .422902     0.93   0.353    -.4366324    1.222492
                    3317  |  -.3432547   .3888149    -0.88   0.377    -1.105952    .4194424
                    3320  |  -.5343228   .3961688    -1.35   0.178    -1.311445    .2427997
                    3330  |   -.371184   .3889137    -0.95   0.340    -1.134075     .391707
                          |
                    _cons |  -.4424832   .3135169    -1.41   0.158    -1.057476    .1725098
-------------------------------------------------------------------------------------------

. lincom c.votes_gunw2LPP3N + 1.method#c.votes_gunw2LPP3N 

 ( 1)  votes_gunw2LPP3N + 1b.method#co.votes_gunw2LPP3N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .8952889   .1180187     7.59   0.000      .663784    1.126794
------------------------------------------------------------------------------

. matrix EV[1,1] = r(estimate)

. matrix EV[1,2] = r(se)

. lincom c.votes_gunw2LPP3N + 2.method#c.votes_gunw2LPP3N 

 ( 1)  votes_gunw2LPP3N + 2.method#c.votes_gunw2LPP3N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9661228   .1291404     7.48   0.000     .7128017    1.219444
------------------------------------------------------------------------------

. matrix EV[1,3] = r(estimate)

. matrix EV[1,4] = r(se)

. lincom c.votes_gunw2LPP3N + 3.method#c.votes_gunw2LPP3N 

 ( 1)  votes_gunw2LPP3N + 3.method#c.votes_gunw2LPP3N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.602241   .2006049     7.99   0.000     1.208735    1.995746
------------------------------------------------------------------------------

. matrix EV[1,5] = r(estimate)

. matrix EV[1,6] = r(se)

. 
. 
. regress don_C_wall_inST c.votes_wall_inw2LPP3N##i.method  i.block

      Source |       SS           df       MS      Number of obs   =     1,502
-------------+----------------------------------   F(45, 1456)     =      5.78
       Model |  241.159036        45  5.35908968   Prob > F        =    0.0000
    Residual |  1350.49872     1,456  .927540332   R-squared       =    0.1515
-------------+----------------------------------   Adj R-squared   =    0.1253
       Total |  1591.65776     1,501  1.06039824   Root MSE        =    .96309

-----------------------------------------------------------------------------------------------
              don_C_wall_inST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------------+----------------------------------------------------------------
         votes_wall_inw2LPP3N |   .4404119   .1261488     3.49   0.000     .1929591    .6878648
                              |
                       method |
                     Likert+  |  -.0870219   .1015188    -0.86   0.391    -.2861607    .1121169
                        QVSR  |  -.1937914   .1140504    -1.70   0.090    -.4175119    .0299292
                              |
method#c.votes_wall_inw2LPP3N |
                     Likert+  |   .1838359   .1541258     1.19   0.233    -.1184965    .4861683
                        QVSR  |   .3057228    .186676     1.64   0.102    -.0604598    .6719054
                              |
                        block |
                        1120  |   -.207303   .4022953    -0.52   0.606    -.9964433    .5818372
                        1130  |   .2456794   .4883216     0.50   0.615    -.7122097    1.203568
                        1210  |   -.050684   .3596992    -0.14   0.888    -.7562681    .6549001
                        1221  |  -.2068697   .3970501    -0.52   0.602     -.985721    .5719817
                        1222  |  -.2351097   .3633787    -0.65   0.518    -.9479114    .4776919
                        1223  |  -.4412002    .360841    -1.22   0.222    -1.149024    .2666236
                        1231  |   .0851731   .5094155     0.17   0.867    -.9140936     1.08444
                        1232  |   -.091631   .4142879    -0.22   0.825     -.904296     .721034
                        1233  |  -.2327368    .364743    -0.64   0.524    -.9482146    .4827411
                        1311  |  -.2448705   .4374511    -0.56   0.576    -1.102972    .6132313
                        1312  |   .0015908   .3875858     0.00   0.997    -.7586954    .7618769
                        1313  |  -.3638031   .3620099    -1.00   0.315     -1.07392    .3463136
                        1321  |  -.3839609    .337031    -1.14   0.255    -1.045079    .2771573
                        1322  |  -.2877062   .3448135    -0.83   0.404    -.9640906    .3886782
                        1323  |  -.5450668    .343063    -1.59   0.112    -1.218017    .1278838
                        1331  |  -.0656621   .3919738    -0.17   0.867    -.8345558    .7032316
                        1332  |  -.1935567   .3860946    -0.50   0.616    -.9509178    .5638045
                        1333  |  -.1367434   .3852894    -0.35   0.723     -.892525    .6190382
                        2010  |   .0869145   .4857241     0.18   0.858    -.8658792    1.039708
                        2020  |  -.7387533   .5428676    -1.36   0.174    -1.803639    .3261328
                        2030  |  -.2977957   .4118334    -0.72   0.470    -1.105646    .5100545
                        3115  |   .2972064   .3401446     0.87   0.382    -.3700194    .9644322
                        3116  |   .0367984     .33792     0.11   0.913    -.6260637    .6996604
                        3117  |   .1475884   .3275637     0.45   0.652    -.4949589    .7901356
                        3120  |   -.370202   .4360855    -0.85   0.396    -1.225625     .485221
                        3135  |  -.0301552   .4018465    -0.08   0.940    -.8184151    .7581046
                        3136  |   -.212359   .4257967    -0.50   0.618    -1.047599    .6228814
                        3137  |  -.2416527   .3765442    -0.64   0.521    -.9802798    .4969745
                        3215  |   .0552313   .3423248     0.16   0.872    -.6162713    .7267338
                        3216  |  -.0896991   .3517862    -0.25   0.799     -.779761    .6003629
                        3217  |  -.2028124   .3463784    -0.59   0.558    -.8822663    .4766415
                        3220  |  -.1613148   .4019068    -0.40   0.688     -.949693    .6270634
                        3235  |   -.141478   .3563373    -0.40   0.691    -.8404672    .5575113
                        3236  |  -.2389061   .3941451    -0.61   0.545    -1.012059    .5342469
                        3237  |    -.26708   .4551463    -0.59   0.557    -1.159893    .6257326
                        3315  |  -.0088082   .3977752    -0.02   0.982     -.789082    .7714655
                        3316  |  -.1431654   .4543529    -0.32   0.753    -1.034422    .7480908
                        3317  |  -.3605376   .4177291    -0.86   0.388    -1.179953    .4588775
                        3320  |  -.1411295   .4302468    -0.33   0.743    -.9850994    .7028404
                        3330  |  -.4425387   .4183244    -1.06   0.290    -1.263122    .3780441
                              |
                        _cons |   -.052655   .3368954    -0.16   0.876    -.7135072    .6081972
-----------------------------------------------------------------------------------------------

. lincom c.votes_wall_inw2LPP3N + 1.method#c.votes_wall_inw2LPP3N 

 ( 1)  votes_wall_inw2LPP3N + 1b.method#co.votes_wall_inw2LPP3N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4404119   .1261488     3.49   0.000     .1929591    .6878648
------------------------------------------------------------------------------

. matrix EV[2,1] = r(estimate)

. matrix EV[2,2] = r(se)

. lincom c.votes_wall_inw2LPP3N + 2.method#c.votes_wall_inw2LPP3N 

 ( 1)  votes_wall_inw2LPP3N + 2.method#c.votes_wall_inw2LPP3N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6242478    .143402     4.35   0.000     .3429512    .9055445
------------------------------------------------------------------------------

. matrix EV[2,3] = r(estimate)

. matrix EV[2,4] = r(se)

. lincom c.votes_wall_inw2LPP3N + 3.method#c.votes_wall_inw2LPP3N 

 ( 1)  votes_wall_inw2LPP3N + 3.method#c.votes_wall_inw2LPP3N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7461347   .1874144     3.98   0.000     .3785036    1.113766
------------------------------------------------------------------------------

. matrix EV[2,5] = r(estimate)

. matrix EV[2,6] = r(se)

. 
. 
. regress writing_minWNST c.abs_votes_minWw2LPP3N##i.method  i.block

      Source |       SS           df       MS      Number of obs   =     1,559
-------------+----------------------------------   F(45, 1513)     =      2.00
       Model |  88.1289307        45  1.95842068   Prob > F        =    0.0001
    Residual |  1481.22231     1,513  .978996903   R-squared       =    0.0562
-------------+----------------------------------   Adj R-squared   =    0.0281
       Total |  1569.35124     1,558  1.00728578   Root MSE        =    .98944

------------------------------------------------------------------------------------------------
               writing_minWNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
         abs_votes_minWw2LPP3N |   .4318578   .1122196     3.85   0.000     .2117354    .6519802
                               |
                        method |
                      Likert+  |   .0970605   .1080398     0.90   0.369    -.1148631    .3089842
                         QVSR  |   .1215866   .1154625     1.05   0.292     -.104897    .3480702
                               |
method#c.abs_votes_minWw2LPP3N |
                      Likert+  |   .0120582   .1615479     0.07   0.941    -.3048235    .3289399
                         QVSR  |   .0864232   .2463561     0.35   0.726    -.3968124    .5696588
                               |
                         block |
                         1120  |   -.423204   .4080301    -1.04   0.300    -1.223569    .3771605
                         1130  |  -.0998849     .49891    -0.20   0.841    -1.078513    .8787436
                         1210  |   .1877409   .3654233     0.51   0.607    -.5290489    .9045308
                         1221  |  -.0057488   .3944949    -0.01   0.988    -.7795637    .7680661
                         1222  |  -.1089283   .3661877    -0.30   0.766    -.8272177     .609361
                         1223  |   .1825725   .3613981     0.51   0.614    -.5263218    .8914667
                         1231  |  -.3066919   .5216636    -0.59   0.557    -1.329952    .7165686
                         1232  |  -.1580431   .4298155    -0.37   0.713     -1.00114    .6850541
                         1233  |  -.1941362   .3712628    -0.52   0.601    -.9223805    .5341082
                         1311  |   .4820627   .4455832     1.08   0.279    -.3919634    1.356089
                         1312  |  -.0683638   .3974368    -0.17   0.863    -.8479493    .7112217
                         1313  |     .00923   .3668746     0.03   0.980    -.7104066    .7288666
                         1321  |  -.2675912   .3370535    -0.79   0.427    -.9287328    .3935504
                         1322  |   .2462991   .3468258     0.71   0.478    -.4340112    .9266095
                         1323  |    .156159   .3445034     0.45   0.650     -.519596    .8319139
                         1331  |  -.1046767     .39445    -0.27   0.791    -.8784034      .66905
                         1332  |  -.1389634   .3917267    -0.35   0.723    -.9073483    .6294215
                         1333  |  -.1317723   .3918231    -0.34   0.737    -.9003462    .6368017
                         2010  |  -.4043062   .4810472    -0.84   0.401    -1.347896     .539284
                         2020  |   .8171251   .5521158     1.48   0.139    -.2658682    1.900118
                         2030  |  -.2299394   .4293167    -0.54   0.592    -1.072058    .6121795
                         3115  |   .0617393   .3493209     0.18   0.860    -.6234652    .7469437
                         3116  |   -.049927   .3461119    -0.14   0.885     -.728837    .6289829
                         3117  |  -.0885372   .3367773    -0.26   0.793     -.749137    .5720625
                         3120  |    .427513   .4450695     0.96   0.337    -.4455056    1.300532
                         3135  |   -.127355   .4130593    -0.31   0.758    -.9375844    .6828744
                         3136  |  -.2709728   .4292277    -0.63   0.528    -1.112917    .5709716
                         3137  |    .094103   .3897209     0.24   0.809    -.6703475    .8585534
                         3215  |  -.0634588   .3508744    -0.18   0.857    -.7517106     .624793
                         3216  |  -.1664264   .3603683    -0.46   0.644    -.8733008     .540448
                         3217  |  -.1694685   .3542773    -0.48   0.632    -.8643951    .5254582
                         3220  |  -.0705163   .3972841    -0.18   0.859    -.8498022    .7087696
                         3235  |  -.1955475   .3627685    -0.54   0.590    -.9071298    .5160348
                         3236  |  -.2499116    .404128    -0.62   0.536    -1.042622    .5427989
                         3237  |  -.2290932    .466675    -0.49   0.624    -1.144492    .6863052
                         3315  |   .2666294   .4079291     0.65   0.513     -.533537    1.066796
                         3316  |   .1580502   .4549187     0.35   0.728    -.7342879    1.050388
                         3317  |  -.0632975   .4295182    -0.15   0.883    -.9058117    .7792166
                         3320  |  -.1233301    .444818    -0.28   0.782    -.9958554    .7491952
                         3330  |   .3613261   .4236233     0.85   0.394    -.4696249    1.192277
                               |
                         _cons |  -.2331257   .3401377    -0.69   0.493     -.900317    .4340656
------------------------------------------------------------------------------------------------

. lincom c.abs_votes_minWw2LPP3N + 1.method#c.abs_votes_minWw2LPP3N , level(90)

 ( 1)  abs_votes_minWw2LPP3N + 1b.method#co.abs_votes_minWw2LPP3N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4318578   .1122196     3.85   0.000       .24716    .6165557
------------------------------------------------------------------------------

. matrix EV[3,1] = r(estimate)

. matrix EV[3,2] = r(se)

. lincom c.abs_votes_minWw2LPP3N + 2.method#c.abs_votes_minWw2LPP3N , level(90)

 ( 1)  abs_votes_minWw2LPP3N + 2.method#c.abs_votes_minWw2LPP3N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .443916   .1175779     3.78   0.000      .250399     .637433
------------------------------------------------------------------------------

. matrix EV[3,3] = r(estimate)

. matrix EV[3,4] = r(se)

. lincom c.abs_votes_minWw2LPP3N + 3.method#c.abs_votes_minWw2LPP3N , level(90)

 ( 1)  abs_votes_minWw2LPP3N + 3.method#c.abs_votes_minWw2LPP3N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .518281   .2190733     2.37   0.018     .1577167    .8788453
------------------------------------------------------------------------------

. matrix EV[3,5] = r(estimate)

. matrix EV[3,6] = r(se)

. 
. 
. regress writing_abortionNST c.abs_votes_abortion_inw2LPP3N##i.method  i.block, 

      Source |       SS           df       MS      Number of obs   =     1,559
-------------+----------------------------------   F(45, 1513)     =      3.69
       Model |  154.895003        45  3.44211118   Prob > F        =    0.0000
    Residual |  1411.82197     1,513  .933127541   R-squared       =    0.0989
-------------+----------------------------------   Adj R-squared   =    0.0721
       Total |  1566.71697     1,558  1.00559498   Root MSE        =    .96599

-------------------------------------------------------------------------------------------------------
                  writing_abortionNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------------------------------+----------------------------------------------------------------
         abs_votes_abortion_inw2LPP3N |   .4137563   .1149741     3.60   0.000     .1882308    .6392818
                                      |
                               method |
                             Likert+  |   -.108495   .1214959    -0.89   0.372    -.3468133    .1298233
                                QVSR  |   -.242273   .1257133    -1.93   0.054    -.4888638    .0043178
                                      |
method#c.abs_votes_abortion_inw2LPP3N |
                             Likert+  |   .3445934   .1591514     2.17   0.031     .0324126    .6567743
                                QVSR  |   .6794349   .2122423     3.20   0.001     .2631145    1.095755
                                      |
                                block |
                                1120  |  -.0565599   .3983286    -0.14   0.887    -.8378946    .7247748
                                1130  |   .0794802    .486989     0.16   0.870     -.875765    1.034725
                                1210  |    .288305   .3566587     0.81   0.419    -.4112928    .9879028
                                1221  |   .0677641   .3858842     0.18   0.861    -.6891607    .8246888
                                1222  |    .036536   .3574856     0.10   0.919    -.6646839    .7377558
                                1223  |   .2530088   .3528785     0.72   0.473    -.4391741    .9451918
                                1231  |   .5524966   .5095877     1.08   0.278    -.4470765     1.55207
                                1232  |   .2484169   .4199562     0.59   0.554    -.5753411    1.072175
                                1233  |   .3782741   .3625323     1.04   0.297     -.332845    1.089393
                                1311  |    .397852   .4349014     0.91   0.360    -.4552216    1.250925
                                1312  |   .4105617   .3879552     1.06   0.290    -.3504253    1.171549
                                1313  |   .3563883   .3585689     0.99   0.320    -.3469566    1.059733
                                1321  |    .460212   .3292755     1.40   0.162    -.1856728    1.106097
                                1322  |    .221792   .3385409     0.66   0.512    -.4422671    .8858512
                                1323  |   .3650596   .3365208     1.08   0.278    -.2950372    1.025156
                                1331  |   .4346865   .3851646     1.13   0.259    -.3208266      1.1902
                                1332  |   .0635643   .3825351     0.17   0.868     -.686791    .8139196
                                1333  |   .3822649   .3823792     1.00   0.318    -.3677847    1.132314
                                2010  |   .5193391   .4701308     1.10   0.269     -.402838    1.441516
                                2020  |    .520249   .5388496     0.97   0.334    -.5367223     1.57722
                                2030  |   .2002207   .4195049     0.48   0.633    -.6226521    1.023093
                                3115  |   .6067164    .341244     1.78   0.076    -.0626451    1.276078
                                3116  |   .5077665   .3378912     1.50   0.133    -.1550183    1.170551
                                3117  |    .309475   .3288677     0.94   0.347    -.3356099    .9545598
                                3120  |   .3233972    .434678     0.74   0.457     -.529238    1.176032
                                3135  |  -.0086696   .4030215    -0.02   0.983    -.7992096    .7818704
                                3136  |   .4377904   .4190009     1.04   0.296    -.3840937    1.259675
                                3137  |  -.0209579   .3802071    -0.06   0.956    -.7667467     .724831
                                3215  |   .2974174   .3425339     0.87   0.385    -.3744742     .969309
                                3216  |   .5157934   .3517064     1.47   0.143    -.1740903    1.205677
                                3217  |   .2114147   .3458215     0.61   0.541    -.4669257    .8897551
                                3220  |  -.1318233   .3878413    -0.34   0.734    -.8925869    .6289403
                                3235  |   .0376304   .3547356     0.11   0.916    -.6581953     .733456
                                3236  |   .1095756   .3952674     0.28   0.782    -.6657544    .8849056
                                3237  |   .2407974    .455677     0.53   0.597    -.6530282    1.134623
                                3315  |   .0359383    .398411     0.09   0.928    -.7455581    .8174347
                                3316  |   .1521818   .4445737     0.34   0.732    -.7198644    1.024228
                                3317  |  -.2384001   .4192266    -0.57   0.570    -1.060727    .5839267
                                3320  |   .0818463   .4342471     0.19   0.851    -.7699439    .9336364
                                3330  |   .3923163   .4131076     0.95   0.342    -.4180079    1.202641
                                      |
                                _cons |  -.5806801   .3373394    -1.72   0.085    -1.242383    .0810223
-------------------------------------------------------------------------------------------------------

. lincom c.abs_votes_abortion_inw2LPP3N + 1.method#c.abs_votes_abortion_inw2LPP3N , level(90)

 ( 1)  abs_votes_abortion_inw2LPP3N + 1b.method#co.abs_votes_abortion_inw2LPP3N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4137563   .1149741     3.60   0.000     .2245249    .6029878
------------------------------------------------------------------------------

. matrix EV[4,1] = r(estimate)

. matrix EV[4,2] = r(se)

. lincom c.abs_votes_abortion_inw2LPP3N + 2.method#c.abs_votes_abortion_inw2LPP3N , level(90)

 ( 1)  abs_votes_abortion_inw2LPP3N + 2.method#c.abs_votes_abortion_inw2LPP3N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7583497   .1116281     6.79   0.000     .5746254    .9420741
------------------------------------------------------------------------------

. matrix EV[4,3] = r(estimate)

. matrix EV[4,4] = r(se)

. lincom c.abs_votes_abortion_inw2LPP3N + 3.method#c.abs_votes_abortion_inw2LPP3N , level(90)

 ( 1)  abs_votes_abortion_inw2LPP3N + 3.method#c.abs_votes_abortion_inw2LPP3N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.093191   .1789277     6.11   0.000      .798701    1.387681
------------------------------------------------------------------------------

. matrix EV[4,5] = r(estimate)

. matrix EV[4,6] = r(se)

. 
. 
. regress punish_FaST c.diffLPP3N##i.method  i.block

      Source |       SS           df       MS      Number of obs   =     1,542
-------------+----------------------------------   F(45, 1496)     =      1.34
       Model |  59.8682549        45  1.33040567   Prob > F        =    0.0668
    Residual |  1484.73865     1,496  .992472359   R-squared       =    0.0388
-------------+----------------------------------   Adj R-squared   =    0.0098
       Total |   1544.6069     1,541  1.00234063   Root MSE        =    .99623

------------------------------------------------------------------------------------
       punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
         diffLPP3N |   .2536402   .2361063     1.07   0.283    -.2094943    .7167748
                   |
            method |
          Likert+  |  -.1763746   .2003305    -0.88   0.379    -.5693331    .2165838
             QVSR  |  -.1999328   .1998987    -1.00   0.317    -.5920442    .1921787
                   |
method#c.diffLPP3N |
          Likert+  |   .2935067   .3430128     0.86   0.392    -.3793305    .9663438
             QVSR  |   .4789939   .3459769     1.38   0.166    -.1996575    1.157645
                   |
             block |
             1120  |  -.3310659   .4110756    -0.81   0.421    -1.137412    .4752799
             1130  |  -.1477843    .502927    -0.29   0.769    -1.134301    .8387327
             1210  |  -.0949166   .3663452    -0.26   0.796    -.8135213    .6236881
             1221  |   -.395439   .4002302    -0.99   0.323    -1.180511    .3896328
             1222  |   .0588306    .371182     0.16   0.874    -.6692618     .786923
             1223  |   .2651306   .3628102     0.73   0.465    -.4465401    .9768014
             1231  |    .444459   .5262596     0.84   0.398    -.5878261    1.476744
             1232  |  -.1137695   .4329223    -0.26   0.793    -.9629686    .7354296
             1233  |  -.1714469   .3739315    -0.46   0.647    -.9049326    .5620388
             1311  |   -.311601   .4483194    -0.70   0.487    -1.191002    .5678003
             1312  |  -.3167187    .400373    -0.79   0.429    -1.102071    .4686334
             1313  |  -.2709482   .3696219    -0.73   0.464    -.9959804     .454084
             1321  |  -.0086417   .3398048    -0.03   0.980    -.6751863    .6579028
             1322  |   .0122505   .3490151     0.04   0.972    -.6723603    .6968614
             1323  |   .2081515   .3473848     0.60   0.549    -.4732614    .8895645
             1331  |  -.1770432   .3970649    -0.45   0.656    -.9559062    .6018199
             1332  |  -.1806845   .4005124    -0.45   0.652    -.9663101     .604941
             1333  |  -.2067935    .389648    -0.53   0.596    -.9711079    .5575209
             2010  |  -.4178771    .484671    -0.86   0.389    -1.368584    .5328298
             2020  |   .6906075   .5565448     1.24   0.215    -.4010835    1.782299
             2030  |  -.0278757   .4257515    -0.07   0.948    -.8630089    .8072576
             3115  |   .2455593   .3515372     0.70   0.485    -.4439987    .9351174
             3116  |   .0234181   .3490133     0.07   0.947    -.6611892    .7080255
             3117  |  -.0349798   .3392387    -0.10   0.918    -.7004138    .6304541
             3120  |  -.2956747   .4492767    -0.66   0.511    -1.176954    .5856044
             3135  |  -.0710223   .4158103    -0.17   0.864    -.8866554    .7446108
             3136  |   .0162756   .4397711     0.04   0.970    -.8463578    .8789091
             3137  |   .0526642   .3922159     0.13   0.893    -.7166873    .8220157
             3215  |   .2448557   .3537594     0.69   0.489    -.4490613    .9387728
             3216  |  -.1668231   .3621486    -0.46   0.645     -.877196    .5435498
             3217  |   .1518514   .3589854     0.42   0.672    -.5523169    .8560196
             3220  |  -.0043296   .4041908    -0.01   0.991    -.7971705    .7885112
             3235  |   .0143916    .366056     0.04   0.969    -.7036459    .7324291
             3236  |  -.1469779   .4069835    -0.36   0.718    -.9452967     .651341
             3237  |  -.3784913   .4703257    -0.80   0.421    -1.301059    .5440765
             3315  |    .279878   .4072554     0.69   0.492    -.5189743     1.07873
             3316  |   .2401028   .4480206     0.54   0.592    -.6387125    1.118918
             3317  |  -.1247793   .4327177    -0.29   0.773    -.9735771    .7240184
             3320  |  -.1288703   .4480445    -0.29   0.774    -1.007732    .7499918
             3330  |   .3805917   .4396367     0.87   0.387    -.4817781    1.242962
                   |
             _cons |   -.158663   .3588455    -0.44   0.658    -.8625568    .5452309
------------------------------------------------------------------------------------

. lincom c.diffLPP3N + 1.method#c.diffLPP3N , level(90)

 ( 1)  diffLPP3N + 1b.method#co.diffLPP3N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2536402   .2361063     1.07   0.283    -.1349607    .6422412
------------------------------------------------------------------------------

. matrix EV[5,1] = r(estimate)

. matrix EV[5,2] = r(se)

. lincom c.diffLPP3N + 2.method#c.diffLPP3N , level(90)

 ( 1)  diffLPP3N + 2.method#c.diffLPP3N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .5471469   .2572357     2.13   0.034     .1237696    .9705242
------------------------------------------------------------------------------

. matrix EV[5,3] = r(estimate)

. matrix EV[5,4] = r(se)

. lincom c.diffLPP3N + 3.method#c.diffLPP3N , level(90)

 ( 1)  diffLPP3N + 3.method#c.diffLPP3N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7326341   .2588086     2.83   0.005     .3066681      1.1586
------------------------------------------------------------------------------

. matrix EV[5,5] = r(estimate)

. matrix EV[5,6] = r(se)

. 
. 
. regress proportionST c.diffLPP3N##i.method i.block

      Source |       SS           df       MS      Number of obs   =     1,521
-------------+----------------------------------   F(45, 1475)     =      1.97
       Model |  86.2915936        45  1.91759097   Prob > F        =    0.0002
    Residual |  1436.32402     1,475  .973778998   R-squared       =    0.0567
-------------+----------------------------------   Adj R-squared   =    0.0279
       Total |  1522.61562     1,520   1.0017208   Root MSE        =     .9868

------------------------------------------------------------------------------------
      proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
         diffLPP3N |   .4737162   .2347876     2.02   0.044      .013163    .9342694
                   |
            method |
          Likert+  |  -.2144004   .1996092    -1.07   0.283    -.6059485    .1771477
             QVSR  |  -.3846788   .2000536    -1.92   0.055    -.7770987    .0077411
                   |
method#c.diffLPP3N |
          Likert+  |   .3384122   .3416164     0.99   0.322    -.3316935    1.008518
             QVSR  |   .9229285   .3459192     2.67   0.008     .2443825    1.601474
                   |
             block |
             1120  |  -.4248264   .4277862    -0.99   0.321     -1.26396    .4143077
             1130  |  -.6414147   .5117869    -1.25   0.210    -1.645322     .362493
             1210  |  -.2329446   .3816317    -0.61   0.542    -.9815433     .515654
             1221  |  -.7774368   .4132751    -1.88   0.060    -1.588106    .0332328
             1222  |  -.3241998   .3858438    -0.84   0.401    -1.081061    .4326613
             1223  |  -.1741882   .3781795    -0.46   0.645    -.9160152    .5676388
             1231  |   .3274323   .5348618     0.61   0.541    -.7217386    1.376603
             1232  |  -.3531328   .4445017    -0.79   0.427    -1.225056      .51879
             1233  |  -.5570318   .3885145    -1.43   0.152    -1.319132     .205068
             1311  |  -.6007964   .4592061    -1.31   0.191    -1.501563    .2999702
             1312  |  -.6067617   .4167573    -1.46   0.146    -1.424262    .2107383
             1313  |  -.6145623   .3846542    -1.60   0.110     -1.36909    .1399652
             1321  |  -.3931134   .3564842    -1.10   0.270    -1.092383    .3061565
             1322  |  -.2970405   .3650759    -0.81   0.416    -1.013164    .4190828
             1323  |   -.242392   .3639173    -0.67   0.505    -.9562426    .4714585
             1331  |     -.5807   .4103776    -1.42   0.157    -1.385686    .2242858
             1332  |  -.6251471   .4138716    -1.51   0.131    -1.436987    .1866926
             1333  |  -.3882454   .4031719    -0.96   0.336    -1.179097    .4026058
             2010  |  -.2470078   .5124864    -0.48   0.630    -1.252288     .758272
             2020  |  -.0597069    .563671    -0.11   0.916    -1.165389    1.045975
             2030  |  -.2652684   .4377343    -0.61   0.545    -1.123916    .5933797
             3115  |  -.0131723   .3681725    -0.04   0.971    -.7353697     .709025
             3116  |  -.2051365   .3655646    -0.56   0.575    -.9222185    .5119455
             3117  |  -.2208954   .3562818    -0.62   0.535    -.9197685    .4779776
             3120  |  -.5671415   .4608721    -1.23   0.219    -1.471176    .3368932
             3135  |   -.483202   .4283663    -1.13   0.259    -1.323474      .35707
             3136  |   -.348919   .4510459    -0.77   0.439    -1.233679    .5358406
             3137  |   .0085402   .4110444     0.02   0.983    -.7977535     .814834
             3215  |  -.0551891    .369783    -0.15   0.881    -.7805456    .6701673
             3216  |  -.3817873   .3781016    -1.01   0.313    -1.123461    .3598867
             3217  |  -.0608592   .3760499    -0.16   0.871    -.7985088    .6767904
             3220  |  -.4558929   .4171057    -1.09   0.275    -1.274076    .3622906
             3235  |  -.2708969   .3810806    -0.71   0.477    -1.018415    .4766207
             3236  |  -.5011749   .4198825    -1.19   0.233    -1.324805    .3224556
             3237  |  -.7760445   .4806132    -1.61   0.107    -1.718803    .1667136
             3315  |  -.0508606   .4204161    -0.12   0.904    -.8755378    .7738165
             3316  |  -.1006171   .4591636    -0.22   0.827      -1.0013    .8000661
             3317  |  -.2229939   .4446855    -0.50   0.616    -1.095277    .6492895
             3320  |  -.2287176   .4585988    -0.50   0.618    -1.128293    .6708577
             3330  |   .0717336   .4509493     0.16   0.874    -.8128366    .9563038
                   |
             _cons |  -.0000165   .3744815    -0.00   1.000    -.7345896    .7345566
------------------------------------------------------------------------------------

. lincom c.diffLPP3N + 1.method#c.diffLPP3N , level(90)

 ( 1)  diffLPP3N + 1b.method#co.diffLPP3N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4737162   .2347876     2.02   0.044     .0872822    .8601502
------------------------------------------------------------------------------

. matrix EV[6,1] = r(estimate)

. matrix EV[6,2] = r(se)

. lincom c.diffLPP3N + 2.method#c.diffLPP3N , level(90)

 ( 1)  diffLPP3N + 2.method#c.diffLPP3N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .8121284   .2566316     3.16   0.002     .3897417    1.234515
------------------------------------------------------------------------------

. matrix EV[6,3] = r(estimate)

. matrix EV[6,4] = r(se)

. lincom c.diffLPP3N + 3.method#c.diffLPP3N , level(90)

 ( 1)  diffLPP3N + 3.method#c.diffLPP3N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [90% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.396645   .2600475     5.37   0.000     .9686358    1.824654
------------------------------------------------------------------------------

. matrix EV[6,5] = r(estimate)

. matrix EV[6,6] = r(se)

. 
. putexcel set  "$pathtemp/EV", replace
note: file will be replaced when the first putexcel command is issued.

. putexcel A1=matrix(EV) 
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp/EV.xlsx saved

. 
. 
. 
. **** code for the figure 
. 
. 
. preserve

. 
. clear all

. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. import excel EV
(6 vars, 6 obs)

. 
. gen topic = _n

. 
. 
. rename A est1

. rename B se1

. rename C est2

. rename D se2

. rename E est3

. rename F se3

. 
. reshape long est se, i(topic) j(method)    
(j = 1 2 3)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations                6   ->   18          
Number of variables                   7   ->   4           
j variable (3 values)                     ->   method
xij variables:
                         est1 est2 est3   ->   est
                            se1 se2 se3   ->   se
-----------------------------------------------------------------------------

. 
. sort topic method

. gen order = _n

. gen lb = est - 1.96*se

. gen ub = est + 1.96*se

. 
. 
. tw (scatter est order,  legend(off) ylabel(0(0.2)2.2) yline(0, lc(black) lstyle(gs15)) ///
> text(2.2 1.5 "Gun", place(e)) ///
> text(2.2 3.9 "Immigration", place(e)) ///
> text(2.2 7.2 "Minimum", place(e)) ///
> text(2.0 7.4 "Wage", place(e)) ///
> text(2.2 10 "Abortion", place(e)) ///
> text(2.2 12.5 "DG punish (1)", place(e)) ///
> text(2.2 15.8 "DG punish (2)", place(e)) ///
> xlabel(1 "Likert" 2 "Likert +" 3 "QVSR" 4 " " 5 " " 6 " " 7 "Likert" 8 "Likert +" 9 "QVSR" ///
> 10 " " 11 " " 12 " " 13 "Likert" 14 "Likert +" 15 "QVSR" 16 " " 17 " " 18 " " , angle(45)labsize(small) ) ///
> xtitle(" ", size(zero))) ///
> (rcap lb ub order, xline(3.5) xline(6.5) xline(9.5) xline(12.5) xline(15.5))
(note:  named style gs15 not found in class linestyle, default attributes used)

. 
. cd "$pathfig"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig

. graph export "FigD1.pdf", replace 
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/FigD1.pdf saved as PDF format

. 
. restore

. 
. ****************************
. * OUTPUT FIG D1            *
. * SEE "FigD1.pdf" IN "FIG" *
. ****************************
. 
. 
. *----- 
. *-------------------------------
. *---  E. Alternative Approaches to Likert+ -- Table E8
. 
. 
. use "$pathout/dataset_final.dta", replace

. 
. *** recode variables to help with table output 
. rename sex Gender

. recode Gender (1 = 0) (2= 1)
(3,940 changes made to Gender)

. 
. rename wall_IP wall_in_IP

. rename abortion_IP abortion_in_IP

. 
. *** higher value = liberal position
. gen wall_inLp = (-1 * wallLp)
(2,621 missing values generated)

. gen abortion_inLp = (-1 * abortionLp)
(2,622 missing values generated)

. 
. *** take abs value of the first item in Likert + for letter writing task
. gen abs_abortion_in = abs(abortionLp)
(2,622 missing values generated)

. gen abs_minW = abs(minWLp)
(2,620 missing values generated)

. 
. gen Don_gun = don_C_gunST 
(264 missing values generated)

. gen Don_immi = don_C_wall_inST 
(264 missing values generated)

. gen Letter_abor = writing_abortionNST 
(2,391 missing values generated)

. gen Letter_minW = writing_minWNST 
(2,391 missing values generated)

. gen Prox_childB = child2 
(2 missing values generated)

. 
. 
. *** coef for Table E8 + values for "Y-hat linear"
. qui reg Don_gun c.gunLp##c.gun_IP if method == 2

. est store inter_gun

. predict YH_gun
(option xb assumed; fitted values)
(2,622 missing values generated)

. 
. qui reg Don_immi c.wall_inLp##c.wall_in_IP if method == 2

. est store inter_wall_in

. predict YH_wall_in
(option xb assumed; fitted values)
(2,621 missing values generated)

. 
. qui reg Letter_abor c.abs_abortion_in##c.abortion_in_IP if method == 2

. est store inter_abortion_in

. predict YH_abortion_in
(option xb assumed; fitted values)
(2,622 missing values generated)

. 
. qui reg Letter_minW c.abs_minW##c.minW_IP if method == 2

. est store inter_minW

. predict YH_minW
(option xb assumed; fitted values)
(2,620 missing values generated)

. 
. qui reg Gender c.genderLp##c.gender_IP if method == 2

. est store inter_gender

. predict YH_gender
(option xb assumed; fitted values)
(2,622 missing values generated)

. 
. qui reg Prox_childB c.paidLLp##c.paidL_IP if method == 2

. est store inter_paidL

. predict YH_paidL
(option xb assumed; fitted values)
(2,620 missing values generated)

. 
. 
. cd "$pathtab"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/tab

. 
. 
. esttab inter_gun inter_wall_in inter_abortion_in inter_minW inter_gender inter_paidL ///
> using Yhat.tex, style(tex) cells(b(star fmt(2)) se(par(( )) fmt(2))) ///
> starlevels(+ 0.10 * .05 ** .01 *** .001) ///
> ar2 varlabels(_cons cons ///
> gunLp Likert ///
> gun_IP Issue_importance ///
> c.gunLp#c.gun_IP Interaction ///
> wall_inLp Likert ///
> wall_in_IP Issue_importance ///
> c.wall_inLp#c.wall_in_IP Interaction ///
> abs_abortion_in Likert_abs ///
> abortion_in_IP Issue_importance ///
> c.abs_abortion_in#c.abortion_in_IP Interaction ///
> abs_minW Likert_abs ///
> minW_IP Issue_importance ///
> c.abs_minW#c.minW_IP Interaction ///
> genderLp Likert ///
> gender_IP Issue_importance ///
> c.genderLp#c.gender_IP Interaction ///
> paidLLp Likert ///
> paidL_IP Issue_importance ///
> c.paidLLp#c.paidL_IP Interaction ) replace
(output written to Yhat.tex)

. 
. 
. **********************************************************
. * OUTPUT TAB E8                                                          *
. * PLEASE RUN CORRESPONDING LATEX FILE AVAILABLE IN "TAB" *
. **********************************************************
. 
. 
. *----- 
. *-------------------------------
. *---  E. Alternative Approaches to Likert+ -- Figure E2
. 
. 
. 
. 
. *** values for "Y-hat quadratic"
. qui reg don_C_gunST c.gunLp##c.gun_IP##c.gun_IP if method == 2

. predict YH_gun2
(option xb assumed; fitted values)
(2,622 missing values generated)

. 
. qui reg don_C_wall_inST c.wall_inLp##c.wall_in_IP##c.wall_in_IP if method == 2

. predict YH_wall_in2
(option xb assumed; fitted values)
(2,621 missing values generated)

. 
. qui reg writing_abortionNST c.abs_abortion_in##c.abortion_in_I##c.abortion_in_IP if method == 2

. predict YH_abortion_in2
(option xb assumed; fitted values)
(2,622 missing values generated)

. 
. qui reg writing_minWNST c.abs_minW##c.minW_IP##c.minW_IP if method == 2

. predict YH_minW2
(option xb assumed; fitted values)
(2,620 missing values generated)

. 
. qui reg Gender c.genderLp##c.gender_IP##c.gender_IP if method == 2

. predict YH_gender2
(option xb assumed; fitted values)
(2,622 missing values generated)

. 
. qui reg child2 c.paidLLp##c.paidL_IP##c.paidL_IP if method == 2

. predict YH_paidL2
(option xb assumed; fitted values)
(2,620 missing values generated)

. 
. 
. gen diffYH = (YH_gun - YH_wall_in)
(2,622 missing values generated)

. gen diffYH2 = (YH_gun2 - YH_wall_in2)
(2,622 missing values generated)

. 
. 
. 
. ** nornalize Y-had lineaire and quadratic
. 
. local issues  gun wall_in abortion_in minW

. foreach var in `issues' {
  2. gen YH_`var'N = .
  3. egen min`var' = min(YH_`var') if method == 2
  4. egen max`var' = max(YH_`var') if method == 2
  5. replace YH_`var'N  =(YH_`var'  - (min`var')) / (max`var' - (min`var')) if method == 2
  6. drop max`var' min`var'
  7. gen YH_`var'2N = .
  8. egen min`var' = min(YH_`var'2) if method == 2
  9. egen max`var' = max(YH_`var'2) if method == 2
 10. replace YH_`var'2N  =(YH_`var'  - (min`var')) / (max`var' - (min`var')) if method == 2
 11. drop max`var' min`var'
 12. }
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,342 real changes made)
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,342 real changes made)
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,343 real changes made)
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,343 real changes made)
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,342 real changes made)
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,342 real changes made)
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,344 real changes made)
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,344 real changes made)

. 
. 
. local issues  YH YH2

. foreach var in `issues' {
  2. gen diff`var'N = .
  3. egen min`var' = min(diff`var') if method == 2
  4. egen max`var' = max(diff`var') if method == 2
  5. replace diff`var'N  =(diff`var'  - (min`var')) / (max`var' - (min`var')) if method == 2
  6. drop max`var' min`var'
  7. }
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,342 real changes made)
(3,964 missing values generated)
(2,614 missing values generated)
(2,614 missing values generated)
(1,342 real changes made)

. 
. 
. 
. 
. matrix EV = J(6,6,0)

. matrix colnames EV = "ImpO" " " "Add" " " "Multi" " " 

. matrix rownames EV = "Gun-related Donations" ///
> "Immigration-related Donations" ///
> "Minimum Wage-related Writing" /// 
> "Abortion-related Writing" ///
> "Punish abs" ///
> "Punish proportion"

. 
. 
. regress don_C_gunST c.votes_gunw1N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,260
-------------+----------------------------------   F(41, 1218)     =      8.46
       Model |   305.58586        41  7.45331365   Prob > F        =    0.0000
    Residual |  1073.38205     1,218  .881266048   R-squared       =    0.2216
-------------+----------------------------------   Adj R-squared   =    0.1954
       Total |  1378.96791     1,259  1.09528825   Root MSE        =    .93876

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
votes_gunw1N |   .9448141   .0938239    10.07   0.000     .7607397    1.128888
             |
       block |
       1120  |  -.8879698   .4628038    -1.92   0.055    -1.795951    .0200112
       1130  |  -.9939923   .4730934    -2.10   0.036    -1.922161    -.065824
       1210  |  -.7741659   .3896431    -1.99   0.047    -1.538612   -.0097199
       1221  |  -.7917128   .4187926    -1.89   0.059    -1.613348     .029922
       1222  |  -.6392612   .3968007    -1.61   0.107     -1.41775    .1392275
       1223  |  -.7881983   .3906815    -2.02   0.044    -1.554682   -.0217149
       1231  |  -1.071989   .5891528    -1.82   0.069    -2.227856    .0838778
       1232  |  -.6393005   .4539683    -1.41   0.159    -1.529947    .2513461
       1233  |  -.7520963    .398181    -1.89   0.059    -1.533293    .0291005
       1311  |    -1.1733   .4731008    -2.48   0.013    -2.101483   -.2451175
       1312  |  -.6852248   .4216166    -1.63   0.104      -1.5124    .1419506
       1313  |  -.9818606   .4053493    -2.42   0.016    -1.777121   -.1866002
       1321  |   -.822569   .3637534    -2.26   0.024    -1.536222   -.1089163
       1322  |  -.7294675   .3719376    -1.96   0.050    -1.459177    .0002418
       1323  |  -.9625362   .3725807    -2.58   0.010    -1.693507   -.2315651
       1331  |  -.7705943   .4401384    -1.75   0.080    -1.634108    .0929193
       1332  |  -.9334752   .4254177    -2.19   0.028    -1.768108   -.0988426
       1333  |  -.9211282   .4073893    -2.26   0.024    -1.720391   -.1218656
       2010  |  -.5647096   .5223777    -1.08   0.280    -1.589569    .4601502
       2020  |   -.344253   .6479085    -0.53   0.595    -1.615393    .9268875
       2030  |  -.8964306   .4347097    -2.06   0.039    -1.749293   -.0435677
       3115  |  -.1914962   .3757792    -0.51   0.610    -.9287426    .5457501
       3116  |  -.3635092   .3767591    -0.96   0.335    -1.102678    .3756597
       3117  |  -.3120033   .3618472    -0.86   0.389    -1.021916    .3979096
       3120  |  -1.034066   .4626815    -2.23   0.026    -1.941807   -.1263252
       3135  |  -.9481885   .4216166    -2.25   0.025    -1.775364   -.1210131
       3136  |  -.4201357   .4732509    -0.89   0.375    -1.348613    .5083416
       3137  |  -.6751553   .4220099    -1.60   0.110    -1.503102    .1527918
       3215  |  -.5471318   .3775231    -1.45   0.148      -1.2878     .193536
       3216  |  -.4570325   .3800811    -1.20   0.229    -1.202719    .2886539
       3217  |  -.6587601   .3779262    -1.74   0.082    -1.400219    .0826984
       3220  |  -.7951103   .4216493    -1.89   0.060     -1.62235    .0321291
       3235  |  -.8304639   .3869317    -2.15   0.032     -1.58959   -.0713372
       3236  |  -.9168713     .42557    -2.15   0.031    -1.751803   -.0819396
       3237  |  -.8995109    .473148    -1.90   0.058    -1.827786    .0287646
       3315  |  -.5108592   .4400979    -1.16   0.246    -1.374293    .3525748
       3316  |  -.2513257   .4734555    -0.53   0.596    -1.180205    .6775531
       3317  |  -.6609479   .4410221    -1.50   0.134    -1.526195    .2042994
       3320  |  -1.485519   .5239091    -2.84   0.005    -2.513383   -.4576542
       3330  |  -.8318406   .4183091    -1.99   0.047    -1.652527   -.0111544
             |
       _cons |   .0420805   .3601341     0.12   0.907    -.6644715    .7486325
------------------------------------------------------------------------------

. lincom c.votes_gunw1N 

 ( 1)  votes_gunw1N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9448141   .0938239    10.07   0.000     .7607397    1.128888
------------------------------------------------------------------------------

. matrix EV[1,1] = r(estimate)

. matrix EV[1,2] = r(se)

. regress don_C_gunST c.votes_gunw1LPP2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,260
-------------+----------------------------------   F(41, 1218)     =      8.55
       Model |  308.278948        41  7.51899872   Prob > F        =    0.0000
    Residual |  1070.68896     1,218  .879054975   R-squared       =    0.2236
-------------+----------------------------------   Adj R-squared   =    0.1974
       Total |  1378.96791     1,259  1.09528825   Root MSE        =    .93758

----------------------------------------------------------------------------------
     don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
votes_gunw1LPP2N |   .9378068   .0916406    10.23   0.000     .7580159    1.117598
                 |
           block |
           1120  |  -.8927825   .4622049    -1.93   0.054    -1.799589    .0140237
           1130  |  -.9884516   .4724975    -2.09   0.037    -1.915451   -.0614523
           1210  |  -.7793374   .3891574    -2.00   0.045    -1.542831   -.0158442
           1221  |  -.8069271   .4181702    -1.93   0.054    -1.627341    .0134867
           1222  |    -.65282   .3963316    -1.65   0.100    -1.430388    .1247483
           1223  |   -.796841   .3901788    -2.04   0.041    -1.562338   -.0313439
           1231  |  -1.083578   .5884488    -1.84   0.066    -2.238063     .070908
           1232  |  -.6332571   .4534076    -1.40   0.163    -1.522804    .2562894
           1233  |  -.7594509   .3976879    -1.91   0.056     -1.53968    .0207783
           1311  |  -1.174661   .4725057    -2.49   0.013    -2.101677   -.2476461
           1312  |  -.6981408   .4211035    -1.66   0.098    -1.524309    .1280279
           1313  |  -.9973039   .4048037    -2.46   0.014    -1.791494   -.2031141
           1321  |  -.8261322   .3632376    -2.27   0.023    -1.538773   -.1134913
           1322  |  -.7423519   .3713941    -2.00   0.046    -1.470995   -.0137088
           1323  |  -.9570503   .3721214    -2.57   0.010     -1.68712   -.2269803
           1331  |  -.7796795   .4395735    -1.77   0.076    -1.642085    .0827258
           1332  |   -.940146   .4248803    -2.21   0.027    -1.773724   -.1065676
           1333  |  -.9301551   .4068706    -2.29   0.022      -1.7284   -.1319101
           2010  |  -.5826425   .5217559    -1.12   0.264    -1.606282    .4409975
           2020  |  -.3188843   .6471407    -0.49   0.622    -1.588518    .9507498
           2030  |  -.9064921   .4341354    -2.09   0.037    -1.758228    -.054756
           3115  |   -.208976   .3753838    -0.56   0.578    -.9454465    .5274946
           3116  |  -.3814413   .3763594    -1.01   0.311    -1.119826    .3569433
           3117  |  -.3340678   .3615056    -0.92   0.356     -1.04331    .3751749
           3120  |  -1.034942   .4620977    -2.24   0.025    -1.941538    -.128346
           3135  |  -.9542088   .4210939    -2.27   0.024    -1.780359   -.1280589
           3136  |  -.4268415   .4726694    -0.90   0.367    -1.354178     .500495
           3137  |  -.6939364   .4215529    -1.65   0.100    -1.520987    .1331139
           3215  |  -.5664734   .3771287    -1.50   0.133    -1.306367    .1734205
           3216  |  -.4739261   .3796612    -1.25   0.212    -1.218789    .2709364
           3217  |  -.6777404    .377529    -1.80   0.073     -1.41842    .0629389
           3220  |  -.8133779   .4211525    -1.93   0.054    -1.639643    .0128869
           3235  |  -.8496152   .3864587    -2.20   0.028    -1.607814   -.0914165
           3236  |  -.9246889   .4250525    -2.18   0.030    -1.758605   -.0907727
           3237  |  -.9298387   .4725157    -1.97   0.049    -1.856874   -.0028037
           3315  |  -.5374402   .4395596    -1.22   0.222    -1.399818    .3249379
           3316  |  -.2666819    .472911    -0.56   0.573    -1.194493    .6611287
           3317  |  -.6728562   .4405173    -1.53   0.127    -1.537113    .1914005
           3320  |  -1.495583   .5231283    -2.86   0.004    -2.521915   -.4692503
           3330  |  -.8457881   .4178181    -2.02   0.043    -1.665511   -.0260652
                 |
           _cons |   .0517093   .3593702     0.14   0.886     -.653344    .7567626
----------------------------------------------------------------------------------

. lincom c.votes_gunw1LPP2N 

 ( 1)  votes_gunw1LPP2N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9378068   .0916406    10.23   0.000     .7580159    1.117598
------------------------------------------------------------------------------

. matrix EV[1,3] = r(estimate)

. matrix EV[1,4] = r(se)

. regress don_C_gunST c.votes_gunw1LPP3N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,260
-------------+----------------------------------   F(41, 1218)     =      9.02
       Model |  321.120136        41  7.83219843   Prob > F        =    0.0000
    Residual |  1057.84777     1,218  .868512127   R-squared       =    0.2329
-------------+----------------------------------   Adj R-squared   =    0.2070
       Total |  1378.96791     1,259  1.09528825   Root MSE        =    .93194

----------------------------------------------------------------------------------
     don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
votes_gunw1LPP3N |    1.08975   .0991577    10.99   0.000     .8952116    1.284289
                 |
           block |
           1120  |  -.8514871   .4595135    -1.85   0.064    -1.753013    .0500387
           1130  |  -.9860279   .4696548    -2.10   0.036     -1.90745   -.0646058
           1210  |  -.7652728   .3868077    -1.98   0.048    -1.524156   -.0063895
           1221  |  -.7975682   .4156308    -1.92   0.055       -1.613    .0178635
           1222  |  -.6195068     .39387    -1.57   0.116    -1.392246    .1532322
           1223  |  -.7791625     .38785    -2.01   0.045    -1.540091   -.0182342
           1231  |  -1.123247   .5849996    -1.92   0.055    -2.270966    .0244712
           1232  |  -.6045191   .4507264    -1.34   0.180    -1.488805    .2797673
           1233  |  -.7347075   .3952764    -1.86   0.063    -1.510206    .0407907
           1311  |  -1.137103   .4696958    -2.42   0.016    -2.058606   -.2156007
           1312  |  -.6278175   .4185241    -1.50   0.134    -1.448926    .1932905
           1313  |  -.9686773   .4024166    -2.41   0.016    -1.758184   -.1791708
           1321  |  -.7979557   .3611168    -2.21   0.027    -1.506436   -.0894757
           1322  |  -.7074201   .3692644    -1.92   0.056    -1.431885    .0170448
           1323  |  -.9336602   .3699063    -2.52   0.012    -1.659384    -.207936
           1331  |  -.7552097   .4369559    -1.73   0.084    -1.612479      .10206
           1332  |  -.8963164   .4223584    -2.12   0.034    -1.724947   -.0676857
           1333  |  -.8843456   .4044698    -2.19   0.029     -1.67788   -.0908108
           2010  |  -.5676104   .5185739    -1.09   0.274    -1.585008    .4497868
           2020  |  -.3557144   .6431713    -0.55   0.580    -1.617561    .9061321
           2030  |   -.894399   .4315352    -2.07   0.038    -1.741034   -.0477643
           3115  |   -.193479   .3729848    -0.52   0.604    -.9252429     .538285
           3116  |  -.3831596   .3740434    -1.02   0.306    -1.117001    .3506813
           3117  |  -.3381704   .3592647    -0.94   0.347    -1.043017    .3666758
           3120  |  -.9953823   .4593772    -2.17   0.030    -1.896641   -.0941238
           3135  |  -.9335165   .4185372    -2.23   0.026     -1.75465   -.1123826
           3136  |  -.4202032   .4697903    -0.89   0.371    -1.341891    .5014848
           3137  |  -.6988916   .4189777    -1.67   0.096     -1.52089    .1231064
           3215  |  -.5545785    .374751    -1.48   0.139    -1.289808    .1806505
           3216  |  -.4738419   .3773373    -1.26   0.209    -1.214145    .2664613
           3217  |  -.6861466   .3752367    -1.83   0.068    -1.422329    .0500354
           3220  |  -.7966692     .41858    -1.90   0.057    -1.617887    .0245485
           3235  |  -.8236129    .384119    -2.14   0.032    -1.577221   -.0700046
           3236  |  -.8832904   .4223898    -2.09   0.037    -1.711983   -.0545981
           3237  |  -.9214501   .4696784    -1.96   0.050    -1.842919    .0000183
           3315  |  -.5150062   .4369025    -1.18   0.239    -1.372171    .3421587
           3316  |  -.2852722   .4700855    -0.61   0.544    -1.207539    .6369949
           3317  |  -.7046667   .4379391    -1.61   0.108    -1.563865    .1545319
           3320  |  -1.469246   .5199745    -2.83   0.005    -2.489391    -.449101
           3330  |  -.8323157   .4152497    -2.00   0.045       -1.647   -.0176317
                 |
           _cons |  -.0479741   .3581309    -0.13   0.893    -.7505961    .6546479
----------------------------------------------------------------------------------

. lincom c.votes_gunw1LPP3N 

 ( 1)  votes_gunw1LPP3N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    1.08975   .0991577    10.99   0.000     .8952116    1.284289
------------------------------------------------------------------------------

. matrix EV[1,5] = r(estimate)

. matrix EV[1,6] = r(se)

. 
. 
. regress don_C_wall_inST c.votes_wall_inw1N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,261
-------------+----------------------------------   F(41, 1219)     =      5.91
       Model |   222.25237        41   5.4207895   Prob > F        =    0.0000
    Residual |  1118.74556     1,219  .917756816   R-squared       =    0.1657
-------------+----------------------------------   Adj R-squared   =    0.1377
       Total |  1340.99793     1,260  1.06428407   Root MSE        =      .958

----------------------------------------------------------------------------------
 don_C_wall_inST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
votes_wall_inw1N |   .6351148   .1209856     5.25   0.000     .3977516    .8724779
                 |
           block |
           1120  |  -.2730834   .4749445    -0.57   0.565    -1.204883     .658716
           1130  |   -.466373   .4842054    -0.96   0.336    -1.416341    .4835953
           1210  |  -.3316995   .3976449    -0.83   0.404    -1.111844    .4484447
           1221  |  -.7523201   .4290578    -1.75   0.080    -1.594094    .0894535
           1222  |  -.4956535   .4101898    -1.21   0.227     -1.30041    .3091028
           1223  |  -.7409988   .4020043    -1.84   0.066    -1.529696    .0476983
           1231  |   .0886281   .6008445     0.15   0.883    -1.090176    1.267432
           1232  |  -.2577327    .464888    -0.55   0.579    -1.169802    .6543365
           1233  |  -.3640193   .4083521    -0.89   0.373     -1.16517    .4371315
           1311  |  -.3178329   .4834298    -0.66   0.511     -1.26628    .6306138
           1312  |  -.4915011   .4304573    -1.14   0.254    -1.336021    .3530183
           1313  |  -.3292532   .4149268    -0.79   0.428    -1.143303    .4847967
           1321  |  -.6819422   .3760533    -1.81   0.070    -1.419726    .0558412
           1322  |  -.4605859   .3836379    -1.20   0.230     -1.21325    .2920779
           1323  |  -.7536381   .3853558    -1.96   0.051    -1.509672    .0023961
           1331  |  -.4687433   .4511262    -1.04   0.299    -1.353813    .4163265
           1332  |  -.3304957   .4364596    -0.76   0.449    -1.186791    .5257997
           1333  |  -.4385521    .417222    -1.05   0.293    -1.257105    .3800007
           2010  |   .1041194   .5331518     0.20   0.845    -.9418776    1.150116
           2020  |  -.2405544   .6659129    -0.36   0.718    -1.547017    1.065908
           2030  |  -.1235949    .443568    -0.28   0.781    -.9938362    .7466464
           3115  |  -.1131306   .3832442    -0.30   0.768     -.865022    .6387609
           3116  |   -.099998   .3841453    -0.26   0.795    -.8536573    .6536613
           3117  |  -.1486367   .3690085    -0.40   0.687    -.8725988    .5753254
           3120  |  -.4842231    .475643    -1.02   0.309    -1.417393    .4489466
           3135  |  -.0736137    .430228    -0.17   0.864    -.9176831    .7704558
           3136  |  -.4307422   .4827971    -0.89   0.372    -1.377948    .5164632
           3137  |  -.4546505   .4302273    -1.06   0.291    -1.298719    .3894176
           3215  |  -.0706098   .3848755    -0.18   0.854    -.8257017    .6844822
           3216  |  -.3664442   .3876371    -0.95   0.345    -1.126954    .3940656
           3217  |  -.3258069   .3853716    -0.85   0.398    -1.081872    .4302583
           3220  |  -.6795794   .4329272    -1.57   0.117    -1.528945    .1697857
           3235  |  -.2774352   .3950588    -0.70   0.483    -1.052506    .4976353
           3236  |  -.5088317   .4345593    -1.17   0.242    -1.361399    .3437354
           3237  |   -.056899   .4831819    -0.12   0.906    -1.004859    .8910614
           3315  |   .1255093   .4491161     0.28   0.780    -.7556169    1.006635
           3316  |  -.2050925   .4828621    -0.42   0.671    -1.152425    .7422404
           3317  |  -.7419743   .4491161    -1.65   0.099      -1.6231    .1391519
           3320  |  -.2124115   .5361157    -0.40   0.692    -1.264223    .8394003
           3330  |  -.4131001   .4267799    -0.97   0.333    -1.250405    .4242045
                 |
           _cons |   .0324412   .3726633     0.09   0.931    -.6986914    .7635739
----------------------------------------------------------------------------------

. lincom c.votes_wall_inw1N 

 ( 1)  votes_wall_inw1N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6351148   .1209856     5.25   0.000     .3977516    .8724779
------------------------------------------------------------------------------

. matrix EV[2,1] = r(estimate)

. matrix EV[2,2] = r(se)

. regress don_C_wall_inST c.votes_wall_inw1LPP2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,261
-------------+----------------------------------   F(41, 1219)     =      5.90
       Model |  222.151347        41  5.41832553   Prob > F        =    0.0000
    Residual |  1118.84658     1,219  .917839689   R-squared       =    0.1657
-------------+----------------------------------   Adj R-squared   =    0.1376
       Total |  1340.99793     1,260  1.06428407   Root MSE        =    .95804

--------------------------------------------------------------------------------------
     don_C_wall_inST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
votes_wall_inw1LPP2N |    .611718   .1167674     5.24   0.000     .3826307    .8408053
                     |
               block |
               1120  |  -.2678176   .4750883    -0.56   0.573    -1.199899    .6642639
               1130  |  -.4752398    .484106    -0.98   0.326    -1.425013    .4745336
               1210  |  -.3395462   .3976487    -0.85   0.393    -1.119698    .4406055
               1221  |  -.7546829   .4290272    -1.76   0.079    -1.596397    .0870308
               1222  |  -.5013379   .4100559    -1.22   0.222    -1.305831    .3031556
               1223  |  -.7429161   .4019887    -1.85   0.065    -1.531583    .0457504
               1231  |   .0940082    .600911     0.16   0.876    -1.084926    1.272943
               1232  |  -.2633348   .4648254    -0.57   0.571    -1.175281    .6486116
               1233  |  -.3704641   .4082578    -0.91   0.364     -1.17143    .4305017
               1311  |  -.3184128   .4834485    -0.66   0.510    -1.266896    .6300706
               1312  |  -.5059286   .4303959    -1.18   0.240    -1.350328    .3384703
               1313  |  -.3351568   .4148602    -0.81   0.419    -1.149076    .4787624
               1321  |  -.6796161   .3761733    -1.81   0.071    -1.417635    .0584028
               1322  |  -.4607107   .3836698    -1.20   0.230    -1.213437    .2920158
               1323  |  -.7511412   .3854846    -1.95   0.052    -1.507428    .0051456
               1331  |  -.4710888   .4511125    -1.04   0.297    -1.356132    .4139542
               1332  |  -.3263034   .4365721    -0.75   0.455    -1.182819    .5302128
               1333  |  -.4366996    .417277    -1.05   0.296     -1.25536    .3819611
               2010  |   .0976779   .5331467     0.18   0.855     -.948309    1.143665
               2020  |  -.2331014   .6661352    -0.35   0.726        -1.54    1.073797
               2030  |  -.1325335    .443555    -0.30   0.765    -1.002749    .7376825
               3115  |  -.1268983   .3833719    -0.33   0.741    -.8790402    .6252435
               3116  |  -.1144808   .3842337    -0.30   0.766    -.8683136    .6393519
               3117  |  -.1579906   .3691119    -0.43   0.669    -.8821557    .5661746
               3120  |  -.4801271   .4757746    -1.01   0.313    -1.413555    .4533007
               3135  |  -.0665718   .4302448    -0.15   0.877    -.9106742    .7775305
               3136  |  -.4331099   .4828159    -0.90   0.370    -1.380352    .5141324
               3137  |  -.4603036   .4302448    -1.07   0.285    -1.304406    .3837988
               3215  |  -.0828008   .3849307    -0.22   0.830     -.838001    .6723994
               3216  |  -.3729556   .3876756    -0.96   0.336    -1.133541    .3876298
               3217  |   -.334968   .3854358    -0.87   0.385    -1.091159    .4212232
               3220  |  -.6754247   .4330469    -1.56   0.119    -1.525025    .1741753
               3235  |  -.2871074   .3950227    -0.73   0.467    -1.062107    .4878923
               3236  |  -.5082699   .4345855    -1.17   0.242    -1.360888    .3443485
               3237  |   -.067484   .4831275    -0.14   0.889    -1.015338    .8803697
               3315  |    .107735   .4491441     0.24   0.810    -.7734462    .9889163
               3316  |  -.2076693   .4828933    -0.43   0.667    -1.155063    .7397247
               3317  |  -.7450438   .4491359    -1.66   0.097    -1.626209    .1361212
               3320  |  -.2159621   .5360795    -0.40   0.687    -1.267703    .8357788
               3330  |  -.4182418   .4267848    -0.98   0.327    -1.255556    .4190725
                     |
               _cons |   .0418409   .3723008     0.11   0.911    -.6885805    .7722624
--------------------------------------------------------------------------------------

. lincom c.votes_wall_inw1LPP2N 

 ( 1)  votes_wall_inw1LPP2N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .611718   .1167674     5.24   0.000     .3826307    .8408053
------------------------------------------------------------------------------

. matrix EV[2,3] = r(estimate)

. matrix EV[2,4] = r(se)

. regress don_C_wall_inST c.votes_wall_inw1LPP3N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,261
-------------+----------------------------------   F(41, 1219)     =      5.88
       Model |  221.463093        41  5.40153886   Prob > F        =    0.0000
    Residual |  1119.53483     1,219  .918404294   R-squared       =    0.1651
-------------+----------------------------------   Adj R-squared   =    0.1371
       Total |  1340.99793     1,260  1.06428407   Root MSE        =    .95833

--------------------------------------------------------------------------------------
     don_C_wall_inST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
votes_wall_inw1LPP3N |     .65748   .1272922     5.17   0.000     .4077438    .9072161
                     |
               block |
               1120  |  -.2794657   .4750678    -0.59   0.556    -1.211507    .6525756
               1130  |  -.4640438   .4844571    -0.96   0.338    -1.414506    .4864184
               1210  |  -.3363882   .3977765    -0.85   0.398    -1.116791    .4440142
               1221  |   -.755988   .4292704    -1.76   0.078    -1.598179    .0862028
               1222  |  -.5131757   .4099645    -1.25   0.211     -1.31749    .2911386
               1223  |  -.7507681   .4020104    -1.87   0.062    -1.539477    .0379409
               1231  |   .1215823   .6013358     0.20   0.840    -1.058186     1.30135
               1232  |  -.2737233    .464848    -0.59   0.556    -1.185714    .6382676
               1233  |    -.38132   .4082362    -0.93   0.350    -1.182244    .4196034
               1311  |  -.3396562   .4834178    -0.70   0.482    -1.288079    .6087669
               1312  |  -.4866787   .4306488    -1.13   0.259    -1.331574    .3582163
               1313  |  -.3323669   .4150683    -0.80   0.423    -1.146694    .4819606
               1321  |  -.6894555   .3761174    -1.83   0.067    -1.427365    .0484537
               1322  |  -.4710541    .383608    -1.23   0.220    -1.223659     .281551
               1323  |  -.7586042   .3855184    -1.97   0.049    -1.514957   -.0022509
               1331  |  -.4972447   .4508547    -1.10   0.270    -1.381782    .3872926
               1332  |  -.3411495   .4364771    -0.78   0.435    -1.197479    .5151801
               1333  |  -.4434017   .4173376    -1.06   0.288    -1.262181    .3753779
               2010  |   .1118402   .5333861     0.21   0.834    -.9346163    1.158297
               2020  |  -.2351434   .6664317    -0.35   0.724    -1.542624    1.072337
               2030  |  -.1312302    .443698    -0.30   0.767    -1.001727    .7392662
               3115  |  -.1244771   .3834796    -0.32   0.746    -.8768303    .6278761
               3116  |  -.0999902   .3842837    -0.26   0.795     -.853921    .6539407
               3117  |  -.1539266   .3691993    -0.42   0.677    -.8782632    .5704099
               3120  |  -.4894871   .4757993    -1.03   0.304    -1.422963    .4439893
               3135  |  -.0871557   .4303973    -0.20   0.840    -.9315572    .7572459
               3136  |  -.4297553   .4829692    -0.89   0.374    -1.377298    .5177877
               3137  |  -.4573529   .4303778    -1.06   0.288    -1.301716    .3870105
               3215  |  -.0756611   .3850267    -0.20   0.844    -.8310495    .6797273
               3216  |  -.3687032   .3877822    -0.95   0.342    -1.129498    .3920913
               3217  |  -.3303074   .3855336    -0.86   0.392     -1.08669    .4260755
               3220  |  -.6942257   .4328546    -1.60   0.109    -1.543448    .1549969
               3235  |  -.2797577   .3951903    -0.71   0.479    -1.055086    .4955708
               3236  |  -.5023221   .4347852    -1.16   0.248    -1.355332    .3506882
               3237  |  -.0480599   .4834387    -0.10   0.921     -.996524    .9004042
               3315  |   .1162396   .4492754     0.26   0.796    -.7651991    .9976783
               3316  |  -.1990368    .483015    -0.41   0.680     -1.14667     .748596
               3317  |   -.751244   .4492754    -1.67   0.095    -1.632683    .1301947
               3320  |  -.2147534    .536356    -0.40   0.689    -1.267037    .8375298
               3330  |  -.4090569   .4269456    -0.96   0.338    -1.246687    .4285728
                     |
               _cons |   .0255391   .3734536     0.07   0.945     -.707144    .7582223
--------------------------------------------------------------------------------------

. lincom c.votes_wall_inw1LPP3N 

 ( 1)  votes_wall_inw1LPP3N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |     .65748   .1272922     5.17   0.000     .4077438    .9072161
------------------------------------------------------------------------------

. matrix EV[2,5] = r(estimate)

. matrix EV[2,6] = r(se)

. 
. 
. regress writing_minWNST c.abs_votes_minWw1N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       542
-------------+----------------------------------   F(41, 500)      =      0.95
       Model |  53.8394404        41  1.31315708   Prob > F        =    0.5575
    Residual |  689.273856       500  1.37854771   R-squared       =    0.0725
-------------+----------------------------------   Adj R-squared   =   -0.0036
       Total |  743.113296       541  1.37359204   Root MSE        =    1.1741

-----------------------------------------------------------------------------------
  writing_minWNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
abs_votes_minWw1N |   .3156274   .1638925     1.93   0.055    -.0063754    .6376302
                  |
            block |
            1120  |  -.3870562   .8302971    -0.47   0.641    -2.018357    1.244245
            1130  |  -.4775567   1.072707    -0.45   0.656    -2.585125    1.630012
            1210  |   .0326367   .7520829     0.04   0.965    -1.444995    1.510269
            1221  |  -.2655393    .857464    -0.31   0.757    -1.950216    1.419137
            1222  |   .0452389   .7471986     0.06   0.952    -1.422797    1.513275
            1223  |   .4531535   .7520928     0.60   0.547    -1.024498    1.930805
            1231  |  -.4144312   1.071872    -0.39   0.699    -2.520359    1.691497
            1232  |  -.0606496   .8969495    -0.07   0.946    -1.822904    1.701605
            1233  |   -.069403   .7426049    -0.09   0.926    -1.528414    1.389608
            1311  |  -.0591288   .8969495    -0.07   0.947    -1.821383    1.703126
            1312  |  -.1840077   .8112326    -0.23   0.821    -1.777852    1.409837
            1313  |  -.0918778   .7649337    -0.12   0.904    -1.594758    1.411003
            1321  |  -.2378586   .6946162    -0.34   0.732    -1.602585    1.126868
            1322  |   .3559976   .7041544     0.51   0.613    -1.027469    1.739464
            1323  |   .0565103   .7041298     0.08   0.936    -1.326907    1.439928
            1331  |   .2313904   .7730281     0.30   0.765    -1.287393    1.750174
            1332  |   .1129346   .7957995     0.14   0.887    -1.450588    1.676458
            1333  |    -.04131   .7829556    -0.05   0.958    -1.579598    1.496978
            2010  |  -.4775567   1.072707    -0.45   0.656    -2.585125    1.630012
            2020  |   2.862198   1.072707     2.67   0.008     .7546293    4.969766
            2030  |   .2418114    .857464     0.28   0.778    -1.442865    1.926488
            3115  |   .3947633   .7132943     0.55   0.580     -1.00666    1.796187
            3116  |  -.0077042   .7090989    -0.01   0.991    -1.400885    1.385476
            3117  |   .0552406   .6928069     0.08   0.936    -1.305931    1.416412
            3120  |    1.17914   .9596572     1.23   0.220    -.7063176    3.064597
            3135  |   .0900897   .8323972     0.11   0.914    -1.545338    1.725517
            3136  |  -.2636919   .8982463    -0.29   0.769    -2.028494     1.50111
            3137  |   .1433189   .7971484     0.18   0.857    -1.422854    1.709492
            3215  |   .0665206   .7207332     0.09   0.927    -1.349518    1.482559
            3216  |    -.08791   .7271512    -0.12   0.904    -1.516558    1.340738
            3217  |   .1078705   .7227823     0.15   0.881    -1.312194    1.527935
            3220  |  -.0048951   .8104709    -0.01   0.995    -1.597243    1.587453
            3235  |  -.2471835   .7426274    -0.33   0.739    -1.706238    1.211871
            3236  |   .1298296   .8974485     0.14   0.885    -1.633405    1.893064
            3237  |  -.3287647   .9592217    -0.34   0.732    -2.213367    1.555837
            3315  |   .2712482   .8102905     0.33   0.738    -1.320746    1.863242
            3316  |  -.0371055   .8574974    -0.04   0.966    -1.721848    1.647637
            3317  |   .2249193   .8582572     0.26   0.793    -1.461316    1.911154
            3320  |   .0446112   .9586615     0.05   0.963     -1.83889    1.928112
            3330  |   1.086773   .8306744     1.31   0.191    -.5452699    2.718815
                  |
            _cons |  -.2198098   .6834883    -0.32   0.748    -1.562673    1.123053
-----------------------------------------------------------------------------------

. lincom c.abs_votes_minWw1N 

 ( 1)  abs_votes_minWw1N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3156274   .1638925     1.93   0.055    -.0063754    .6376302
------------------------------------------------------------------------------

. matrix EV[3,1] = r(estimate)

. matrix EV[3,2] = r(se)

. regress writing_minWNST c.abs_votes_minWw1LPP2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       542
-------------+----------------------------------   F(41, 500)      =      0.99
       Model |  55.6207742        41  1.35660425   Prob > F        =    0.4973
    Residual |  687.492522       500  1.37498504   R-squared       =    0.0748
-------------+----------------------------------   Adj R-squared   =   -0.0010
       Total |  743.113296       541  1.37359204   Root MSE        =    1.1726

---------------------------------------------------------------------------------------
      writing_minWNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
abs_votes_minWw1LPP2N |   .3678661   .1642862     2.24   0.026     .0450898    .6906424
                      |
                block |
                1120  |  -.4043337   .8293283    -0.49   0.626    -2.033731    1.225064
                1130  |  -.5160114    1.07183    -0.48   0.630    -2.621857    1.589834
                1210  |   .0217044   .7511612     0.03   0.977    -1.454117    1.497526
                1221  |  -.2693848   .8563617    -0.31   0.753    -1.951896    1.413126
                1222  |   .0360141   .7462735     0.05   0.962    -1.430204    1.502232
                1223  |   .4506766   .7510947     0.60   0.549    -1.025014    1.926367
                1231  |  -.4240449   1.070518    -0.40   0.692    -2.527312    1.679223
                1232  |  -.0889694   .8960586    -0.10   0.921    -1.849473    1.671535
                1233  |  -.0843678   .7417182    -0.11   0.909    -1.541636    1.372901
                1311  |  -.0989443   .8962399    -0.11   0.912    -1.859805    1.661916
                1312  |  -.1877139    .809852    -0.23   0.817    -1.778846    1.403418
                1313  |  -.1067354   .7640515    -0.14   0.889    -1.607882    1.394412
                1321  |  -.2516536   .6937454    -0.36   0.717    -1.614669    1.111362
                1322  |   .3385718   .7032195     0.48   0.630    -1.043057    1.720201
                1323  |    .035125   .7033101     0.05   0.960    -1.346682    1.416932
                1331  |    .209696    .772209     0.27   0.786    -1.307478     1.72687
                1332  |   .1235909   .7947446     0.16   0.876     -1.43786    1.685041
                1333  |  -.0457242   .7818516    -0.06   0.953    -1.581844    1.490395
                2010  |  -.4930198   1.071355    -0.46   0.646    -2.597931    1.611892
                2020  |   2.846735   1.071355     2.66   0.008      .741823    4.951646
                2030  |   .2379659   .8563617     0.28   0.781    -1.444545    1.920477
                3115  |    .384343   .7124152     0.54   0.590    -1.015353    1.784039
                3116  |    -.02236   .7082906    -0.03   0.975    -1.413953    1.369233
                3117  |   .0275099   .6923057     0.04   0.968    -1.332677    1.387697
                3120  |   1.140685   .9589867     1.19   0.235    -.7434551    3.024825
                3135  |   .0755338   .8311907     0.09   0.928    -1.557523    1.708591
                3136  |  -.2978611   .8975868    -0.33   0.740    -2.061368    1.465646
                3137  |   .1134353   .7965114     0.14   0.887    -1.451486    1.678357
                3215  |   .0521984   .7198145     0.07   0.942    -1.362036    1.466432
                3216  |  -.1078903   .7263963    -0.15   0.882    -1.535056    1.319275
                3217  |   .0880535    .722022     0.12   0.903    -1.330517    1.506624
                3220  |    -.01618   .8094817    -0.02   0.984    -1.606585    1.574225
                3235  |  -.2548204   .7416272    -0.34   0.731     -1.71191    1.202269
                3236  |   .0870893   .8969602     0.10   0.923    -1.675186    1.849365
                3237  |  -.3729335   .9586202    -0.39   0.697    -2.256354    1.510487
                3315  |   .2616345    .809285     0.32   0.747    -1.328384    1.851653
                3316  |  -.0294145   .8564142    -0.03   0.973    -1.712029    1.653199
                3317  |   .2014294   .8573952     0.23   0.814    -1.483112    1.885971
                3320  |   .0292834   .9574464     0.03   0.976     -1.85183    1.910397
                3330  |   1.103143   .8297237     1.33   0.184    -.5270317    2.733317
                      |
                _cons |   -.250736    .682823    -0.37   0.714    -1.592292     1.09082
---------------------------------------------------------------------------------------

. lincom c.abs_votes_minWw1LPP2N 

 ( 1)  abs_votes_minWw1LPP2N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3678661   .1642862     2.24   0.026     .0450898    .6906424
------------------------------------------------------------------------------

. matrix EV[3,3] = r(estimate)

. matrix EV[3,4] = r(se)

. regress writing_minWNST c.abs_votes_minWw1LPP3N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       542
-------------+----------------------------------   F(41, 500)      =      0.99
       Model |  55.6148127        41  1.35645885   Prob > F        =    0.4975
    Residual |  687.498483       500  1.37499697   R-squared       =    0.0748
-------------+----------------------------------   Adj R-squared   =   -0.0010
       Total |  743.113296       541  1.37359204   Root MSE        =    1.1726

---------------------------------------------------------------------------------------
      writing_minWNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
abs_votes_minWw1LPP3N |   .3511955   .1569097     2.24   0.026     .0429118    .6594791
                      |
                block |
                1120  |  -.3933296   .8292451    -0.47   0.635    -2.022564    1.235905
                1130  |  -.5104546   1.071712    -0.48   0.634    -2.616068    1.595159
                1210  |   .0470217   .7510672     0.06   0.950    -1.428615    1.522658
                1221  |  -.2477573   .8563581    -0.29   0.772    -1.930261    1.434746
                1222  |   .0379987   .7462542     0.05   0.959    -1.428182    1.504179
                1223  |   .4587619   .7511396     0.61   0.542    -1.017017    1.934541
                1231  |  -.3699764   1.070486    -0.35   0.730    -2.473181    1.733229
                1232  |   -.058946   .8957275    -0.07   0.948      -1.8188    1.700908
                1233  |  -.0630213   .7416243    -0.08   0.932    -1.520105    1.394063
                1311  |  -.0691316   .8958344    -0.08   0.939    -1.829195    1.690932
                1312  |  -.1752004    .810105    -0.22   0.829     -1.76683    1.416429
                1313  |  -.0997994   .7639755    -0.13   0.896    -1.600797    1.401198
                1321  |  -.2347369   .6937225    -0.34   0.735    -1.597707    1.128233
                1322  |   .3567543   .7032424     0.51   0.612     -1.02492    1.738429
                1323  |   .0584756   .7032195     0.08   0.934    -1.323154    1.440105
                1331  |   .2306491   .7720036     0.30   0.765    -1.286122     1.74742
                1332  |   .1270871   .7948243     0.16   0.873     -1.43452    1.688694
                1333  |  -.0529667   .7818051    -0.07   0.946    -1.588995    1.483061
                2010  |  -.4636285   1.070895    -0.43   0.665    -2.567637     1.64038
                2020  |   2.864419   1.071061     2.67   0.008     .7600849    4.968754
                2030  |   .2595933   .8563581     0.30   0.762     -1.42291    1.942097
                3115  |   .4026063   .7123502     0.57   0.572    -.9969623    1.802175
                3116  |  -.0087696    .708153    -0.01   0.990    -1.400092    1.382553
                3117  |   .0478731   .6918922     0.07   0.945    -1.311501    1.407247
                3120  |   1.146242   .9588536     1.20   0.232    -.7376367    3.030121
                3135  |   .0848524   .8309149     0.10   0.919    -1.547663    1.717367
                3136  |  -.2983682   .8976076    -0.33   0.740    -2.061916    1.465179
                3137  |   .1068642   .7967623     0.13   0.893     -1.45855    1.672279
                3215  |    .066933   .7198042     0.09   0.926    -1.347281    1.481147
                3216  |  -.0741442   .7260587    -0.10   0.919    -1.500646    1.352358
                3217  |   .0949512   .7219376     0.13   0.895    -1.323454    1.513356
                3220  |  -.0026093   .8093397    -0.00   0.997    -1.592735    1.587516
                3235  |  -.2530909   .7416351    -0.34   0.733    -1.710196    1.204014
                3236  |   .0987101   .8966928     0.11   0.912     -1.66304     1.86046
                3237  |  -.3592914   .9583399    -0.37   0.708    -2.242161    1.523578
                3315  |   .2889453   .8091741     0.36   0.721    -1.300855    1.878746
                3316  |  -.0258433   .8564399    -0.03   0.976    -1.708508    1.656821
                3317  |   .2215398   .8570019     0.26   0.796    -1.462229    1.905308
                3320  |   .0211981   .9574832     0.02   0.982    -1.859988    1.902384
                3330  |   1.112211   .8298881     1.34   0.181    -.5182862    2.742709
                      |
                _cons |  -.2153664   .6809509    -0.32   0.752    -1.553244    1.122511
---------------------------------------------------------------------------------------

. lincom c.abs_votes_minWw1LPP3N 

 ( 1)  abs_votes_minWw1LPP3N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3511955   .1569097     2.24   0.026     .0429118    .6594791
------------------------------------------------------------------------------

. matrix EV[3,5] = r(estimate)

. matrix EV[3,6] = r(se)

. 
. 
. regress writing_abortionNST c.abs_votes_abortion_inw1N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       541
-------------+----------------------------------   F(41, 499)      =      1.78
       Model |  75.8345538        41  1.84962326   Prob > F        =    0.0027
    Residual |  519.020516       499  1.04012127   R-squared       =    0.1275
-------------+----------------------------------   Adj R-squared   =    0.0558
       Total |   594.85507       540  1.10158346   Root MSE        =    1.0199

------------------------------------------------------------------------------------------
     writing_abortionNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------------+----------------------------------------------------------------
abs_votes_abortion_inw1N |   .7389538   .1342352     5.50   0.000     .4752179     1.00269
                         |
                   block |
                   1120  |  -.4329823   .7216519    -0.60   0.549    -1.850833    .9848683
                   1130  |  -.6300367   .9310466    -0.68   0.499    -2.459291    1.199218
                   1210  |   .4899153   .6540361     0.75   0.454    -.7950886    1.774919
                   1221  |   -.152087    .746487    -0.20   0.839    -1.618732    1.314558
                   1222  |  -.2512414   .6489599    -0.39   0.699    -1.526272    1.023789
                   1223  |   .1632595   .6539372     0.25   0.803     -1.12155    1.448069
                   1231  |   .8616042   .9320782     0.92   0.356    -.9696774    2.692886
                   1232  |   .5699188    .779563     0.73   0.465    -.9617115    2.101549
                   1233  |   .4198427   .6460107     0.65   0.516    -.8493936    1.689079
                   1311  |    .985536   .7789849     1.27   0.206    -.5449586    2.516031
                   1312  |   .2011234   .7038016     0.29   0.775    -1.181656    1.583903
                   1313  |  -.0089373   .6642772    -0.01   0.989    -1.314062    1.296188
                   1321  |   .1841933   .6035511     0.31   0.760    -1.001621    1.370008
                   1322  |  -.0648538   .6118054    -0.11   0.916    -1.266886    1.137178
                   1323  |   .2413631   .6119707     0.39   0.693    -.9609938     1.44372
                   1331  |   .2277512   .6715286     0.34   0.735    -1.091621    1.547123
                   1332  |   .0144887   .6904517     0.02   0.983    -1.342062    1.371039
                   1333  |   .1589939   .6799351     0.23   0.815    -1.176895    1.494882
                   2010  |   2.208699   .9315303     2.37   0.018     .3784935    4.038904
                   2020  |  -.4083505   .9315303    -0.44   0.661    -2.238556    1.421854
                   2030  |   .0374632    .744805     0.05   0.960    -1.425877    1.500804
                   3115  |   .1255201   .6196113     0.20   0.840    -1.091848    1.342889
                   3116  |   .4190244   .6158407     0.68   0.497    -.7909359    1.628985
                   3117  |   .1764839   .6015381     0.29   0.769    -1.005376    1.358343
                   3120  |  -.2077999    .832763    -0.25   0.803    -1.843954    1.428354
                   3135  |  -.3356107   .7211523    -0.47   0.642     -1.75248    1.081258
                   3136  |  -.1853681   .7790138    -0.24   0.812    -1.715919    1.345183
                   3137  |  -.3198776   .6904952    -0.46   0.643    -1.676514    1.036759
                   3215  |  -.2443564   .6260449    -0.39   0.696    -1.474365    .9856523
                   3216  |  -.0296693   .6316206    -0.05   0.963    -1.270633    1.211294
                   3217  |   -.043784   .6282391    -0.07   0.944    -1.278104    1.190536
                   3220  |  -.2469134   .7041081    -0.35   0.726    -1.630295    1.136468
                   3235  |  -.0790944   .6457355    -0.12   0.903     -1.34779    1.189601
                   3236  |  -.2605598   .7811024    -0.33   0.739    -1.795215    1.274095
                   3237  |  -.3529932    .832763    -0.42   0.672    -1.989147    1.283161
                   3315  |  -.0209776   .7049606    -0.03   0.976    -1.406034    1.364079
                   3316  |   .3966173   .7453532     0.53   0.595      -1.0678    1.861035
                   3317  |  -.6300367   .7448567    -0.85   0.398    -2.093478    .8334051
                   3320  |  -.3346724   .8329073    -0.40   0.688     -1.97111    1.301765
                   3330  |   .0010469   .7218322     0.00   0.999    -1.417158    1.419252
                         |
                   _cons |  -.5126579   .5969903    -0.86   0.391    -1.685582    .6602664
------------------------------------------------------------------------------------------

. lincom c.abs_votes_abortion_inw1N 

 ( 1)  abs_votes_abortion_inw1N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7389538   .1342352     5.50   0.000     .4752179     1.00269
------------------------------------------------------------------------------

. matrix EV[4,1] = r(estimate)

. matrix EV[4,2] = r(se)

. regress writing_abortionNST c.abs_votes_abortion_inw1LPP2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       541
-------------+----------------------------------   F(41, 499)      =      1.71
       Model |  73.3675745        41  1.78945304   Prob > F        =    0.0048
    Residual |  521.487495       499  1.04506512   R-squared       =    0.1233
-------------+----------------------------------   Adj R-squared   =    0.0513
       Total |   594.85507       540  1.10158346   Root MSE        =    1.0223

----------------------------------------------------------------------------------------------
         writing_abortionNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------------------+----------------------------------------------------------------
abs_votes_abortion_inw1LPP2N |    .720792   .1367056     5.27   0.000     .4522025    .9893815
                             |
                       block |
                       1120  |  -.4155916   .7235427    -0.57   0.566    -1.837157    1.005974
                       1130  |  -.5957896   .9332179    -0.64   0.523     -2.42931    1.237731
                       1210  |   .5012474    .655771     0.76   0.445    -.7871651     1.78966
                       1221  |  -.1276331   .7487407    -0.17   0.865    -1.598706     1.34344
                       1222  |  -.2301453   .6506004    -0.35   0.724    -1.508399    1.048108
                       1223  |   .1374453   .6553312     0.21   0.834    -1.150103    1.424994
                       1231  |   .8705667   .9344687     0.93   0.352    -.9654114    2.706545
                       1232  |   .5852025   .7815937     0.75   0.454    -.9504176    2.120823
                       1233  |   .4330193   .6477776     0.67   0.504    -.8396884    1.705727
                       1311  |   1.042308   .7807838     1.33   0.183    -.4917211    2.576337
                       1312  |   .1702256   .7054436     0.24   0.809     -1.21578    1.556231
                       1313  |   .0002356   .6658586     0.00   1.000    -1.307996    1.308468
                       1321  |   .1980524   .6050748     0.33   0.744    -.9907558    1.386861
                       1322  |  -.0537733    .613331    -0.09   0.930    -1.258803    1.151256
                       1323  |    .249782   .6135117     0.41   0.684    -.9556024    1.455166
                       1331  |   .2190856   .6731005     0.33   0.745    -1.103375    1.541546
                       1332  |    .015839   .6920912     0.02   0.982    -1.343933    1.375611
                       1333  |   .2062395   .6816921     0.30   0.762    -1.133101     1.54558
                       2010  |   2.246507    .934065     2.41   0.017      .411322    4.081692
                       2020  |  -.3705421    .934065    -0.40   0.692    -2.205727    1.464643
                       2030  |   .0756633   .7466265     0.10   0.919    -1.391256    1.542582
                       3115  |   .1319642   .6210712     0.21   0.832    -1.088273    1.352201
                       3116  |   .4246482   .6173195     0.69   0.492    -.7882175    1.637514
                       3117  |   .1849018   .6029502     0.31   0.759    -.9997323    1.369536
                       3120  |  -.1669645   .8348665    -0.20   0.842    -1.807251    1.473322
                       3135  |  -.3656437   .7228866    -0.51   0.613     -1.78592    1.054633
                       3136  |  -.1943898   .7808461    -0.25   0.804    -1.728541    1.339762
                       3137  |  -.2991631   .6921956    -0.43   0.666     -1.65914    1.060814
                       3215  |   -.234643   .6275386    -0.37   0.709    -1.467587    .9983005
                       3216  |  -.0158101   .6332069    -0.02   0.980     -1.25989     1.22827
                       3217  |  -.0405356   .6298051    -0.06   0.949    -1.277932    1.196861
                       3220  |  -.2464215    .705811    -0.35   0.727    -1.633149    1.140306
                       3235  |  -.0663926    .647452    -0.10   0.918     -1.33846    1.205675
                       3236  |  -.2353936   .7835255    -0.30   0.764    -1.774809    1.304022
                       3237  |  -.3722238    .834711    -0.45   0.656    -2.012205    1.267758
                       3315  |  -.0488228   .7064355    -0.07   0.945    -1.436777    1.339132
                       3316  |   .4131652   .7473015     0.55   0.581     -1.05508     1.88141
                       3317  |  -.6047995   .7465848    -0.81   0.418    -2.071637    .8620376
                       3320  |  -.2830346   .8351774    -0.34   0.735    -1.923932    1.357863
                       3330  |  -.0061942   .7235427    -0.01   0.993     -1.42776    1.415371
                             |
                       _cons |  -.5413854   .6000565    -0.90   0.367    -1.720334    .6375632
----------------------------------------------------------------------------------------------

. lincom c.abs_votes_abortion_inw1LPP2N 

 ( 1)  abs_votes_abortion_inw1LPP2N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .720792   .1367056     5.27   0.000     .4522025    .9893815
------------------------------------------------------------------------------

. matrix EV[4,3] = r(estimate)

. matrix EV[4,4] = r(se)

. regress writing_abortionNST c.abs_votes_abortion_inw1LPP3N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       541
-------------+----------------------------------   F(41, 499)      =      1.97
       Model |   82.732127        41  2.01785676   Prob > F        =    0.0005
    Residual |  512.122943       499  1.02629848   R-squared       =    0.1391
-------------+----------------------------------   Adj R-squared   =    0.0683
       Total |   594.85507       540  1.10158346   Root MSE        =    1.0131

----------------------------------------------------------------------------------------------
         writing_abortionNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------------------+----------------------------------------------------------------
abs_votes_abortion_inw1LPP3N |   .7820316   .1278193     6.12   0.000     .5309013    1.033162
                             |
                       block |
                       1120  |   -.433056   .7167511    -0.60   0.546    -1.841278     .975166
                       1130  |  -.5894623   .9247977    -0.64   0.524     -2.40644    1.227515
                       1210  |   .4775725   .6494456     0.74   0.462    -.7984124    1.753557
                       1221  |  -.1707616   .7410306    -0.23   0.818    -1.626686    1.285163
                       1222  |  -.2411372   .6446431    -0.37   0.709    -1.507687    1.025412
                       1223  |   .1542643   .6493922     0.24   0.812    -1.121616    1.430144
                       1231  |   .8412065   .9255335     0.91   0.364    -.9772164    2.659629
                       1232  |   .5712811   .7742615     0.74   0.461    -.9499333    2.092495
                       1233  |   .4382815   .6416852     0.68   0.495    -.8224563    1.699019
                       1311  |   1.065212   .7737563     1.38   0.169    -.4550098    2.585434
                       1312  |   .1833136   .6990863     0.26   0.793    -1.190202    1.556829
                       1313  |  -.0197352   .6598488    -0.03   0.976     -1.31616    1.276689
                       1321  |   .2012404   .5995631     0.34   0.737    -.9767388     1.37922
                       1322  |  -.0563584    .607726    -0.09   0.926    -1.250375    1.137659
                       1323  |   .2612762   .6079367     0.43   0.668    -.9331549    1.455707
                       1331  |   .2395848   .6670633     0.36   0.720    -1.071014    1.550184
                       1332  |   .0322262   .6858586     0.05   0.963      -1.3153    1.379753
                       1333  |   .2362152   .6756155     0.35   0.727    -1.091186    1.563617
                       2010  |   2.183993   .9251117     2.36   0.019     .3663989    4.001587
                       2020  |   -.433056   .9251117    -0.47   0.640     -2.25065    1.384538
                       2030  |   .0345619   .7398381     0.05   0.963     -1.41902    1.488144
                       3115  |   .1012329   .6155303     0.16   0.869    -1.108118    1.310583
                       3116  |    .399545   .6117033     0.65   0.514    -.8022866    1.601376
                       3117  |   .1572389   .5975647     0.26   0.793    -1.016814    1.331292
                       3120  |   -.239685   .8271681    -0.29   0.772    -1.864846    1.385476
                       3135  |  -.3703676   .7163669    -0.52   0.605    -1.777835    1.037099
                       3136  |  -.1383321    .773944    -0.18   0.858    -1.658923    1.382258
                       3137  |  -.2869523   .6859601    -0.42   0.676    -1.634678    1.060774
                       3215  |  -.2264785   .6218841    -0.36   0.716    -1.448312    .9953555
                       3216  |   .0134455   .6275702     0.02   0.983     -1.21956    1.246451
                       3217  |  -.0439882   .6239501    -0.07   0.944    -1.269881    1.181905
                       3220  |  -.2076646   .6995627    -0.30   0.767    -1.582116    1.166787
                       3235  |  -.0728057   .6413472    -0.11   0.910    -1.332879    1.187268
                       3236  |  -.1984465   .7762596    -0.26   0.798    -1.723587    1.326694
                       3237  |  -.3501213   .8272071    -0.42   0.672    -1.975359    1.275117
                       3315  |  -.0053664   .7001877    -0.01   0.994    -1.381046    1.370313
                       3316  |   .4648941   .7407582     0.63   0.531    -.9904953    1.920283
                       3317  |  -.5946759   .7398408    -0.80   0.422    -2.048263     .858911
                       3320  |  -.2941718   .8274753    -0.36   0.722    -1.919937    1.331593
                       3330  |   .0458553   .7172237     0.06   0.949    -1.363295    1.455006
                             |
                       _cons |  -.5094913   .5914836    -0.86   0.389    -1.671597     .652614
----------------------------------------------------------------------------------------------

. lincom c.abs_votes_abortion_inw1LPP3N 

 ( 1)  abs_votes_abortion_inw1LPP3N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7820316   .1278193     6.12   0.000     .5309013    1.033162
------------------------------------------------------------------------------

. matrix EV[4,5] = r(estimate)

. matrix EV[4,6] = r(se)

. 
. 
. 
. regress punish_FaST c.diffN  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       534
-------------+----------------------------------   F(41, 492)      =      1.24
       Model |  51.5966564        41  1.25845503   Prob > F        =    0.1508
    Residual |   498.74892       492  1.01371732   R-squared       =    0.0938
-------------+----------------------------------   Adj R-squared   =    0.0182
       Total |  550.345577       533   1.0325433   Root MSE        =    1.0068

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       diffN |   .5970371   .2612063     2.29   0.023     .0838195    1.110255
             |
       block |
       1120  |  -1.422584   .7123426    -2.00   0.046    -2.822193   -.0229751
       1130  |  -1.452468   .9236463    -1.57   0.116    -3.267246    .3623097
       1210  |  -1.095346    .640591    -1.71   0.088    -2.353978     .163285
       1221  |  -1.339031   .7364753    -1.82   0.070    -2.786056    .1079937
       1222  |   -1.07727   .6440696    -1.67   0.095    -2.342737    .1881957
       1223  |  -.7839283   .6379299    -1.23   0.220    -2.037331    .4694748
       1231  |  -.4750615   .9219134    -0.52   0.607    -2.286434    1.336311
       1232  |  -1.362912   .7707556    -1.77   0.078    -2.877291    .1514661
       1233  |  -1.081368   .6373503    -1.70   0.090    -2.333632    .1708963
       1311  |  -1.288283   .7692327    -1.67   0.095    -2.799669    .2231034
       1312  |  -1.256299   .6948049    -1.81   0.071     -2.62145    .1088521
       1313  |  -1.297781   .6562208    -1.98   0.049    -2.587122   -.0084402
       1321  |  -.8582123   .5972876    -1.44   0.151    -2.031761    .3153367
       1322  |  -.7931075   .6042072    -1.31   0.190    -1.980252    .3940373
       1323  |  -.9894393   .6047744    -1.64   0.102    -2.177698    .1988199
       1331  |  -1.253963   .6631095    -1.89   0.059    -2.556839    .0489128
       1332  |  -1.303209   .6717316    -1.94   0.053    -2.623025    .0166076
       1333  |  -1.175541   .6716767    -1.75   0.081     -2.49525    .1441672
       2010  |  -1.303209   .9194818    -1.42   0.157    -3.109804    .5033865
       2020  |   .4575673   .9219134     0.50   0.620    -1.353806     2.26894
       2030  |  -.3451924   .7353303    -0.47   0.639    -1.789967    1.099583
       3115  |  -.4410295   .6127406    -0.72   0.472    -1.644941    .7628817
       3116  |  -.8844196   .6088807    -1.45   0.147    -2.080747    .3119076
       3117  |  -.9686366   .5937992    -1.63   0.103    -2.135332    .1980584
       3120  |  -1.263406   .8221237    -1.54   0.125    -2.878713    .3519001
       3135  |  -.6018513   .7119933    -0.85   0.398    -2.000774    .7970713
       3136  |  -.1194074   .8237358    -0.14   0.885    -1.737881    1.499067
       3137  |  -1.415129   .6824923    -2.07   0.039    -2.756088   -.0741699
       3215  |  -.9119798   .6180832    -1.48   0.141    -2.126388    .3024284
       3216  |  -1.191667   .6255314    -1.91   0.057     -2.42071    .0373752
       3217  |  -1.107076   .6214372    -1.78   0.075    -2.328074    .1139219
       3220  |  -1.414087   .6987791    -2.02   0.044    -2.787046   -.0411278
       3235  |  -1.127115   .6367881    -1.77   0.077    -2.378275    .1240445
       3236  |  -.9226943   .7693227    -1.20   0.231    -2.434258     .588869
       3237  |   -1.32311    .822815    -1.61   0.108    -2.939775    .2935547
       3315  |  -.6593997    .695143    -0.95   0.343    -2.025215    .7064154
       3316  |  -.1422616   .7353303    -0.19   0.847    -1.587037    1.302513
       3317  |  -.9226943   .7693227    -1.20   0.231    -2.434258     .588869
       3320  |  -1.027127   .7690941    -1.34   0.182    -2.538241    .4839865
       3330  |   .3233113   .7352885     0.44   0.660    -1.121382    1.768004
             |
       _cons |    .583421   .5957877     0.98   0.328    -.5871811    1.754023
------------------------------------------------------------------------------

. lincom c.diffN 

 ( 1)  diffN = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .5970371   .2612063     2.29   0.023     .0838195    1.110255
------------------------------------------------------------------------------

. matrix EV[5,1] = r(estimate)

. matrix EV[5,2] = r(se)

. regress punish_FaST c.diffLPP2N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       534
-------------+----------------------------------   F(41, 492)      =      1.24
       Model |  51.6797907        41   1.2604827   Prob > F        =    0.1489
    Residual |  498.665786       492  1.01354835   R-squared       =    0.0939
-------------+----------------------------------   Adj R-squared   =    0.0184
       Total |  550.345577       533   1.0325433   Root MSE        =    1.0068

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   diffLPP2N |   .5807846   .2521037     2.30   0.022      .085452    1.076117
             |
       block |
       1120  |  -1.425329   .7123177    -2.00   0.046    -2.824889   -.0257691
       1130  |  -1.452225    .923489    -1.57   0.116    -3.266693    .3622442
       1210  |  -1.085641   .6405935    -1.69   0.091    -2.344277    .1729956
       1221  |  -1.337883   .7363677    -1.82   0.070    -2.784696    .1089307
       1222  |  -1.074128    .643822    -1.67   0.096    -2.339108    .1908515
       1223  |  -.7810606    .637786    -1.22   0.221    -2.034181    .4720597
       1231  |   -.474222   .9217652    -0.51   0.607    -2.285304     1.33686
       1232  |  -1.361477   .7706224    -1.77   0.078    -2.875594    .1526399
       1233  |  -1.075333   .6371837    -1.69   0.092     -2.32727    .1766041
       1311  |  -1.293416   .7692242    -1.68   0.093    -2.804786    .2179535
       1312  |  -1.253876    .694739    -1.80   0.072    -2.618898    .1111452
       1313  |  -1.296304   .6561368    -1.98   0.049    -2.585479   -.0071279
       1321  |  -.8570979   .5972064    -1.44   0.152    -2.030487    .3162917
       1322  |   -.792503   .6041413    -1.31   0.190    -1.979518    .3945121
       1323  |  -.9871405   .6046844    -1.63   0.103    -2.175223    .2009419
       1331  |  -1.249759   .6629946    -1.89   0.060    -2.552409    .0528912
       1332  |  -1.308037   .6717519    -1.95   0.052    -2.627893    .0118196
       1333  |   -1.17562    .671615    -1.75   0.081    -2.495207    .1439676
       2010  |  -1.297954   .9193379    -1.41   0.159    -3.104266    .5083591
       2020  |   .4674816   .9214705     0.51   0.612    -1.343021    2.277984
       2030  |  -.3413241    .735288    -0.46   0.643    -1.786016    1.103368
       3115  |  -.4340687   .6126994    -0.71   0.479    -1.637899    .7697614
       3116  |  -.8794965   .6087925    -1.44   0.149     -2.07565    .3166573
       3117  |  -.9646149   .5937512    -1.62   0.105    -2.131216    .2019858
       3120  |  -1.267704   .8220762    -1.54   0.124    -2.882918    .3475087
       3135  |  -.5975532   .7119582    -0.84   0.402    -1.996407    .8013005
       3136  |   -.114947    .823522    -0.14   0.889    -1.733001    1.503107
       3137  |  -1.409366    .682302    -2.07   0.039    -2.749951   -.0687808
       3215  |  -.9074525   .6180128    -1.47   0.143    -2.121722    .3068173
       3216  |  -1.191818   .6254783    -1.91   0.057    -2.420757    .0371198
       3217  |  -1.103119   .6213809    -1.78   0.076    -2.324006    .1177688
       3220  |  -1.419815   .6989271    -2.03   0.043    -2.793065   -.0465645
       3235  |  -1.126414   .6367333    -1.77   0.078    -2.377466    .1246375
       3236  |    -.91129   .7691234    -1.18   0.237    -2.422462    .5998816
       3237  |  -1.328203   .8228308    -1.61   0.107    -2.944899     .288493
       3315  |  -.6509402   .6952065    -0.94   0.350     -2.01688    .7149998
       3316  |  -.1352402   .7352425    -0.18   0.854    -1.579843    1.309362
       3317  |  -.9249021   .7692822    -1.20   0.230    -2.436386    .5865816
       3320  |  -1.025218   .7690427    -1.33   0.183    -2.536231    .4857953
       3330  |   .3233113   .7352273     0.44   0.660    -1.121261    1.767884
             |
       _cons |   .5915472   .5947592     0.99   0.320    -.5770342    1.760128
------------------------------------------------------------------------------

. lincom c.diffLPP2N

 ( 1)  diffLPP2N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .5807846   .2521037     2.30   0.022      .085452    1.076117
------------------------------------------------------------------------------

. matrix EV[5,3] = r(estimate)

. matrix EV[5,4] = r(se)

. regress punish_FaST c.diffLPP3N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       534
-------------+----------------------------------   F(41, 492)      =      1.26
       Model |  52.1814092        41   1.2727173   Prob > F        =    0.1377
    Residual |  498.164168       492   1.0125288   R-squared       =    0.0948
-------------+----------------------------------   Adj R-squared   =    0.0194
       Total |  550.345577       533   1.0325433   Root MSE        =    1.0062

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   diffLPP3N |   .6638673   .2754652     2.41   0.016     .1226341      1.2051
             |
       block |
       1120  |  -1.426866   .7119438    -2.00   0.046    -2.825691   -.0280407
       1130  |  -1.479547   .9237786    -1.60   0.110    -3.294584    .3354909
       1210  |  -1.094203   .6402168    -1.71   0.088    -2.352099    .1636937
       1221  |  -1.360051   .7364469    -1.85   0.065     -2.80702    .0869182
       1222  |  -1.079176   .6434225    -1.68   0.094    -2.343371     .185019
       1223  |  -.7863537   .6374997    -1.23   0.218    -2.038911     .466204
       1231  |  -.5137246     .92242    -0.56   0.578    -2.326093    1.298644
       1232  |  -1.355072   .7699252    -1.76   0.079    -2.867819    .1576754
       1233  |   -1.08311   .6369493    -1.70   0.090    -2.334587    .1683659
       1311  |  -1.266556   .7685918    -1.65   0.100    -2.776683     .243571
       1312  |  -1.240871   .6943758    -1.79   0.075    -2.605178    .1234371
       1313  |  -1.298492   .6558035    -1.98   0.048    -2.587013    -.009971
       1321  |  -.8627569   .5969601    -1.45   0.149    -2.035663    .3101488
       1322  |     -.7966   .6038649    -1.32   0.188    -1.983072    .3898722
       1323  |  -.9910701   .6044058    -1.64   0.102    -2.178605    .1964648
       1331  |  -1.248614   .6626252    -1.88   0.060    -2.550538    .0533107
       1332  |  -1.298827   .6712226    -1.94   0.054    -2.617644     .019989
       1333  |  -1.169559   .6711559    -1.74   0.082    -2.488245     .149126
       2010  |  -1.302516    .918898    -1.42   0.157    -3.107964    .5029329
       2020  |   .4355009   .9218171     0.47   0.637    -1.375683    2.246685
       2030  |  -.3468756   .7348882    -0.47   0.637    -1.790782    1.097031
       3115  |  -.4339392   .6123906    -0.71   0.479    -1.637163    .7692842
       3116  |  -.8906612   .6085645    -1.46   0.144    -2.086367    .3050448
       3117  |  -.9714595   .5934528    -1.64   0.102    -2.137474    .1945551
       3120  |  -1.291451   .8218364    -1.57   0.117    -2.906193    .3232908
       3135  |  -.5977796   .7115921    -0.84   0.401    -1.995914    .8003548
       3136  |  -.1438379   .8237605    -0.17   0.861     -1.76236    1.474685
       3137  |   -1.41929   .6820911    -2.08   0.038    -2.759461   -.0791197
       3215  |  -.9106765   .6177116    -1.47   0.141    -2.124355    .3030017
       3216  |   -1.19686   .6251479    -1.91   0.056    -2.425149    .0314288
       3217  |  -1.112982   .6210865    -1.79   0.074    -2.333291    .1073275
       3220  |   -1.41316   .6979343    -2.02   0.043    -2.784459   -.0418606
       3235  |  -1.122843    .636407    -1.76   0.078    -2.373254    .1275681
       3236  |  -.9239318   .7688525    -1.20   0.230    -2.434571    .5867077
       3237  |  -1.335709   .8224858    -1.62   0.105    -2.951727    .2803091
       3315  |  -.6541983   .6947689    -0.94   0.347    -2.019278    .7108817
       3316  |  -.1479547   .7349226    -0.20   0.841    -1.591929    1.296019
       3317  |  -.9266979   .7688865    -1.21   0.229    -2.437404    .5840083
       3320  |  -1.024708   .7686489    -1.33   0.183    -2.534947    .4855313
       3330  |   .3196232    .734859     0.43   0.664    -1.124226    1.763472
             |
       _cons |    .553694   .5967082     0.93   0.354    -.6187166    1.726105
------------------------------------------------------------------------------

. lincom c.diffLPP3N

 ( 1)  diffLPP3N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6638673   .2754652     2.41   0.016     .1226341      1.2051
------------------------------------------------------------------------------

. matrix EV[5,5] = r(estimate)

. matrix EV[5,6] = r(se)

. 
. 
. regress proportionST c.diffN  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       528
-------------+----------------------------------   F(41, 486)      =      1.34
       Model |  45.1004166        41  1.10001016   Prob > F        =    0.0847
    Residual |  400.181191       486  .823418088   R-squared       =    0.1013
-------------+----------------------------------   Adj R-squared   =    0.0255
       Total |  445.281607       527  .844936637   Root MSE        =    .90742

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       diffN |   .7187567   .2366608     3.04   0.003      .253752    1.183761
             |
       block |
       1120  |  -1.063126   .6420122    -1.66   0.098    -2.324588    .1983362
       1130  |     -1.138   .8324919    -1.37   0.172    -2.773728    .4977273
       1210  |  -.7070088   .5773416    -1.22   0.221    -1.841402     .427385
       1221  |  -1.001437   .6637696    -1.51   0.132    -2.305649    .3027759
       1222  |  -.6913578   .5805098    -1.19   0.234    -1.831977    .4492612
       1223  |  -.4895397   .5749539    -0.85   0.395    -1.619242    .6401626
       1231  |   .2455597   .8309136     0.30   0.768    -1.387067    1.878186
       1232  |  -1.030187   .6946708    -1.48   0.139    -2.395116     .334742
       1233  |   -.659319    .574426    -1.15   0.252    -1.787984    .4693461
       1311  |  -.9403423   .6932837    -1.36   0.176    -2.302546    .4218611
       1312  |  -.9018375   .6262025    -1.44   0.150    -2.132236     .328561
       1313  |  -.9517771    .591432    -1.61   0.108    -2.113856    .2103022
       1321  |   -.514841   .5385986    -0.96   0.340     -1.57311    .5434283
       1322  |  -.5148251   .5445537    -0.95   0.345    -1.584795    .5551452
       1323  |  -.4336367   .5456157    -0.79   0.427    -1.505694    .6384201
       1331  |  -.8109708   .5976395    -1.36   0.175    -1.985247    .3633055
       1332  |  -.9583113    .605412    -1.58   0.114    -2.147859    .2312368
       1333  |  -.3633611    .605362    -0.60   0.549    -1.552811    .8260888
       2010  |  -.9583113   .8286989    -1.16   0.248    -2.586586    .6699637
       2020  |   .2455597   .8309136     0.30   0.768    -1.387067    1.878186
       2030  |   .4115943   .6627267     0.62   0.535     -.890569    1.713758
       3115  |  -.0282168   .5522408    -0.05   0.959    -1.113291    1.056857
       3116  |  -.4430592   .5495756    -0.81   0.421    -1.522897    .6367784
       3117  |  -.5194138   .5351695    -0.97   0.332    -1.570945    .5321179
       3120  |  -.9103941   .7409505    -1.23   0.220    -2.366256    .5454677
       3135  |  -.4192592   .6416941    -0.65   0.514    -1.680096    .8415779
       3136  |  -.1437513   .7424188    -0.19   0.847    -1.602498    1.314996
       3137  |  -.6923806   .6420819    -1.08   0.281     -1.95398    .5692186
       3215  |  -.6045685   .5570562    -1.09   0.278    -1.699104    .4899674
       3216  |  -.7313404   .5637688    -1.30   0.195    -1.839066    .3763849
       3217  |  -.4814306   .5618267    -0.86   0.392     -1.58534    .6224785
       3220  |  -1.091795   .6298222    -1.73   0.084    -2.329305     .145716
       3235  |  -.6744101   .5739139    -1.18   0.241    -1.802069    .4532488
       3236  |  -.2845001   .6933657    -0.41   0.682    -1.646865    1.077865
       3237  |  -.9822698   .7415801    -1.32   0.186    -2.439369    .4748292
       3315  |  -.4449269   .6265104    -0.71   0.478     -1.67593    .7860766
       3316  |   .1557244   .6627267     0.23   0.814    -1.146439    1.457888
       3317  |  -.2845001   .6933657    -0.41   0.682    -1.646865    1.077865
       3320  |  -.1856711   .6931574    -0.27   0.789    -1.547626    1.176284
       3330  |   .4432178   .6626887     0.67   0.504    -.8588709    1.745306
             |
       _cons |   .0739556   .5370985     0.14   0.891    -.9813662    1.129277
------------------------------------------------------------------------------

. lincom c.diffN 

 ( 1)  diffN = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7187567   .2366608     3.04   0.003      .253752    1.183761
------------------------------------------------------------------------------

. matrix EV[6,1] = r(estimate)

. matrix EV[6,2] = r(se)

. regress proportionST c.diffLPP2N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       528
-------------+----------------------------------   F(41, 486)      =      1.35
       Model |  45.5132109        41  1.11007831   Prob > F        =    0.0775
    Residual |  399.768397       486  .822568717   R-squared       =    0.1022
-------------+----------------------------------   Adj R-squared   =    0.0265
       Total |  445.281607       527  .844936637   Root MSE        =    .90696

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   diffLPP2N |   .7125282   .2283652     3.12   0.002     .2638232    1.161233
             |
       block |
       1120  |  -1.067751   .6417126    -1.66   0.097    -2.328624    .1931229
       1130  |    -1.1425   .8319911    -1.37   0.170    -2.777244    .4922433
       1210  |  -.6947588   .5770947    -1.20   0.229    -1.828667    .4391498
       1221  |  -1.002221   .6633856    -1.51   0.131    -2.305679    .3012365
       1222  |  -.6910285   .5800352    -1.19   0.234    -1.830715    .4486579
       1223  |  -.4880325   .5745751    -0.85   0.396    -1.616991    .6409256
       1231  |   .2428192   .8304211     0.29   0.770     -1.38884    1.874478
       1232  |  -1.031168   .6942503    -1.49   0.138    -2.395271    .3329348
       1233  |  -.6533314   .5740265    -1.14   0.256    -1.781212    .4745487
       1311  |  -.9476685   .6929768    -1.37   0.172    -2.309269     .413932
       1312  |  -.8991593   .6258722    -1.44   0.151    -2.128909    .3305902
       1313  |  -.9512109   .5911003    -1.61   0.108    -2.112639    .2102167
       1321  |  -.5146587   .5382872    -0.96   0.339    -1.572316    .5429989
       1322  |  -.5152381   .5442587    -0.95   0.344    -1.584629    .5541526
       1323  |  -.4324014   .5453045    -0.79   0.428    -1.503847    .6390441
       1331  |  -.8069099    .597277    -1.35   0.177    -1.980474    .3666541
       1332  |  -.9656054   .6051693    -1.60   0.111    -2.154677    .2234659
       1333  |  -.3647521   .6050447    -0.60   0.547    -1.553578    .8240742
       2010  |  -.9532351   .8282103    -1.15   0.250     -2.58055    .6740798
       2020  |   .2539525   .8301527     0.31   0.760    -1.377179    1.885084
       2030  |   .4167514   .6624022     0.63   0.530    -.8847742    1.718277
       3115  |  -.0196516   .5519648    -0.04   0.972    -1.104184     1.06488
       3116  |  -.4381772   .5492595    -0.80   0.425    -1.517394    .6410393
       3117  |  -.5144997   .5348948    -0.96   0.337    -1.565491    .5364921
       3120  |  -.9161243   .7405874    -1.24   0.217    -2.371273    .5390242
       3135  |  -.4135291   .6413851    -0.64   0.519    -1.673759    .8467011
       3136  |  -.1410212   .7419043    -0.19   0.849    -1.598757    1.316715
       3137  |  -.6873044   .6416718    -1.07   0.285    -1.948098     .573489
       3215  |  -.5993718   .5567516    -1.08   0.282    -1.693309    .4945655
       3216  |  -.7312371   .5634772    -1.30   0.195    -1.838389     .375915
       3217  |  -.4764697   .5615323    -0.85   0.397      -1.5798    .6268611
       3220  |  -1.102739   .6296867    -1.75   0.081    -2.339983    .1345057
       3235  |  -.6737333   .5736163    -1.17   0.241    -1.800807    .4533408
       3236  |  -.2717085    .692885    -0.39   0.695    -1.633129    1.089711
       3237  |   -.990346   .7412747    -1.34   0.182    -2.446845    .4661531
       3315  |  -.4333732    .626298    -0.69   0.489    -1.663959    .7972129
       3316  |   .1639272   .6623607     0.25   0.805    -1.137517    1.465371
       3317  |  -.2884084   .6930297    -0.42   0.677    -1.650113    1.073296
       3320  |  -.1826425   .6928115    -0.26   0.792    -1.543918    1.178633
       3330  |   .4432178   .6623468     0.67   0.504    -.8581991    1.744635
             |
       _cons |   .0770698   .5359359     0.14   0.886    -.9759676    1.130107
------------------------------------------------------------------------------

. lincom c.diffLPP2N

 ( 1)  diffLPP2N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7125282   .2283652     3.12   0.002     .2638232    1.161233
------------------------------------------------------------------------------

. matrix EV[6,3] = r(estimate)

. matrix EV[6,4] = r(se)

. regress proportionST c.diffLPP3N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       528
-------------+----------------------------------   F(41, 486)      =      1.41
       Model |  47.2985555        41   1.1536233   Prob > F        =    0.0520
    Residual |  397.983052       486  .818895169   R-squared       =    0.1062
-------------+----------------------------------   Adj R-squared   =    0.0308
       Total |  445.281607       527  .844936637   Root MSE        =    .90493

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   diffLPP3N |   .8630933   .2495798     3.46   0.001     .3727046    1.353482
             |
       block |
       1120  |  -1.073959   .6402654    -1.68   0.094    -2.331989    .1840706
       1130  |  -1.193313   .8308353    -1.44   0.152    -2.825786    .4391596
       1210  |  -.7040853   .5757552    -1.22   0.222    -1.835362    .4271914
       1221  |  -1.037956    .662317    -1.57   0.118    -2.339315     .263402
       1222  |   -.708608   .5786813    -1.22   0.221    -1.845634     .428418
       1223  |  -.5011192    .573326    -0.87   0.383    -1.627623    .6253845
       1231  |   .1794949   .8295953     0.22   0.829    -1.450541    1.809531
       1232  |  -1.031483   .6924219    -1.49   0.137    -2.391993    .3290269
       1233  |  -.6675208   .5728236    -1.17   0.244    -1.793037    .4579956
       1311  |  -.9164041   .6912047    -1.33   0.186    -2.274523    .4417144
       1312  |  -.8830106   .6244606    -1.41   0.158    -2.109987    .3439653
       1313  |  -.9579241   .5897774    -1.62   0.105    -2.116753    .2009043
       1321  |  -.5270115    .537141    -0.98   0.327    -1.582417    .5283939
       1322  |  -.5242037   .5430686    -0.97   0.335    -1.591256    .5428486
       1323  |  -.4390696   .5440929    -0.81   0.420    -1.508134    .6299952
       1331  |  -.8086123   .5959101    -1.36   0.175    -1.979491    .3622659
       1332  |    -.95836   .6036439    -1.59   0.113    -2.144434    .2277141
       1333  |  -.3610097    .603583    -0.60   0.550    -1.546964    .8249448
       2010  |   -.963155   .8263805    -1.17   0.244    -2.586875    .6605648
       2020  |   .2010722    .829045     0.24   0.808    -1.427883    1.830027
       2030  |   .4111295   .6608943     0.62   0.534    -.8874335    1.709692
       3115  |  -.0188924   .5507304    -0.03   0.973    -1.100999    1.063214
       3116  |  -.4553083    .548113    -0.83   0.407    -1.532272    .6216554
       3117  |  -.5231671   .5336994    -0.98   0.327     -1.57181     .525476
       3120  |  -.9487701   .7390907    -1.28   0.200    -2.400978    .5034375
       3135  |  -.4120505   .6399444    -0.64   0.520     -1.66945    .8453488
       3136  |  -.1870035    .740847    -0.25   0.801    -1.642662    1.268655
       3137  |  -.7092117   .6403949    -1.11   0.269    -1.967496    .5490727
       3215  |  -.6043727    .555516    -1.09   0.277    -1.695882    .4871368
       3216  |  -.7368821   .5622032    -1.31   0.191    -1.841531    .3677669
       3217  |  -.4898267   .5602947    -0.87   0.382    -1.590726    .6110723
       3220  |  -1.107004   .6277087    -1.76   0.078    -2.340362     .126354
       3235  |  -.6696217   .5723286    -1.17   0.243    -1.794166    .4549222
       3236  |  -.2911359   .6914427    -0.42   0.674    -1.649722     1.06745
       3237  |   -1.00631   .7396835    -1.36   0.174    -2.459682    .4470628
       3315  |  -.4332402   .6248194    -0.69   0.488    -1.660921    .7944407
       3316  |   .1465994   .6609257     0.22   0.825    -1.152025    1.445224
       3317  |  -.2947321   .6914738    -0.43   0.670    -1.653379    1.063915
       3320  |   -.179653   .6912569    -0.26   0.795    -1.537874    1.178568
       3330  |   .4384228   .6608676     0.66   0.507    -.8600876    1.736933
             |
       _cons |   .0065823   .5368365     0.01   0.990    -1.048225    1.061389
------------------------------------------------------------------------------

. lincom c.diffLPP3N

 ( 1)  diffLPP3N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .8630933   .2495798     3.46   0.001     .3727046    1.353482
------------------------------------------------------------------------------

. matrix EV[6,5] = r(estimate)

. matrix EV[6,6] = r(se)

. 
. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. putexcel set  "$pathtemp/EV", replace
note: file will be replaced when the first putexcel command is issued.

. putexcel A1=matrix(EV) 
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp/EV.xlsx saved

. 
. 
. 
. preserve

. 
. clear all

. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. import excel EV
(6 vars, 6 obs)

. 
. gen topic = _n

. 
. 
. rename A est1

. rename B se1

. rename C est2

. rename D se2

. rename E est3

. rename F se3

. 
. reshape long est se, i(topic) j(method)    
(j = 1 2 3)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations                6   ->   18          
Number of variables                   7   ->   4           
j variable (3 values)                     ->   method
xij variables:
                         est1 est2 est3   ->   est
                            se1 se2 se3   ->   se
-----------------------------------------------------------------------------

. 
. sort topic method

. gen order = _n

. gen lb = est - 1.96*se

. gen ub = est + 1.96*se

. 
. 
. tw (scatter est order,  legend(off) ylabel(0(0.2)2.2) yline(0, lc(black) lstyle(gs15)) ///
> text(2.2 1.5 "Gun", place(e)) ///
> text(2.2 3.9 "Immigration", place(e)) ///
> text(2.2 7.2 "Minimum", place(e)) ///
> text(2.0 7.4 "Wage", place(e)) ///
> text(2.2 10 "Abortion", place(e)) ///
> text(2.2 12.5 "DG punish (1)", place(e)) ///
> text(2.2 15.8 "DG punish (2)", place(e)) ///
> xlabel(1 "ImpO" 2 "Add" 3 "Multi" 4 " " 5 " " 6 " " 7 "ImpO" 8 "Add" 9 "Multi" ///
> 10 " " 11 " " 12 " " 13 "ImpO" 14 "Add" 15 "Multi" 16 " " 17 " " 18 " " , angle(45)labsize(small) ) ///
> xtitle(" ", size(zero))) ///
> (rcap lb ub order, xline(3.5) xline(6.5) xline(9.5) xline(12.5) xline(15.5))
(note:  named style gs15 not found in class linestyle, default attributes used)

. 
. cd "$pathfig"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig

. graph export "FigE2_top.pdf", replace 
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/FigE2_top.pdf saved as PDF format

. 
. restore 

. 
. 
. 
. 
. matrix EV2 = J(6,6,0)

. matrix colnames EV2 = "YHL" " " "YHQ" " " "Multi" " " 

. matrix rownames EV2 = "Gun-related Donations" ///
> "Immigration-related Donations" ///
> "Minimum Wage-related Writing" /// 
> "Abortion-related Writing" ///
> "Punish abs" ///
> "Punish proportion"

. 
. 
. regress don_C_gunST c.YH_gunN i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,260
-------------+----------------------------------   F(41, 1218)     =      9.03
       Model |   321.58075        41  7.84343293   Prob > F        =    0.0000
    Residual |  1057.38716     1,218  .868133954   R-squared       =    0.2332
-------------+----------------------------------   Adj R-squared   =    0.2074
       Total |  1378.96791     1,259  1.09528825   Root MSE        =    .93174

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     YH_gunN |   1.078256   .0978758    11.02   0.000     .8862318    1.270279
             |
       block |
       1120  |  -.8536684   .4594059    -1.86   0.063    -1.754983    .0476464
       1130  |  -.9865374   .4695526    -2.10   0.036    -1.907759   -.0653157
       1210  |   -.763622   .3867229    -1.97   0.049    -1.522339   -.0049051
       1221  |  -.7995319   .4155293    -1.92   0.055    -1.614764    .0157006
       1222  |  -.6095994   .3937703    -1.55   0.122    -1.382143    .1629438
       1223  |  -.7767116   .3877689    -2.00   0.045    -1.537481   -.0159426
       1231  |  -1.134967   .5849274    -1.94   0.053    -2.282544    .0126106
       1232  |  -.5921974   .4506568    -1.31   0.189    -1.476347    .2919523
       1233  |  -.7318895   .3951893    -1.85   0.064    -1.507217    .0434377
       1311  |  -1.134046   .4695971    -2.41   0.016    -2.055355   -.2127374
       1312  |  -.6176811   .4184351    -1.48   0.140    -1.438615    .2032524
       1313  |  -.9649417   .4023371    -2.40   0.017    -1.754292    -.175591
       1321  |  -.7999848    .361019    -2.22   0.027    -1.508273   -.0916968
       1322  |  -.7026623   .3692049    -1.90   0.057     -1.42701    .0216858
       1323  |  -.9364108    .369801    -2.53   0.011    -1.661928   -.2108932
       1331  |  -.7486082   .4368704    -1.71   0.087     -1.60571    .1084938
       1332  |  -.8888877   .4222757    -2.10   0.035    -1.717356   -.0604192
       1333  |  -.8700219   .4044051    -2.15   0.032     -1.66343   -.0766141
       2010  |   -.574125   .5184719    -1.11   0.268    -1.591322    .4430721
       2020  |  -.3428842   .6430494    -0.53   0.594    -1.604492    .9187231
       2030  |  -.8865955   .4314586    -2.05   0.040     -1.73308   -.0401109
       3115  |  -.1932237   .3729006    -0.52   0.604    -.9248224     .538375
       3116  |  -.3825027   .3739572    -1.02   0.307    -1.116174     .351169
       3117  |  -.3394769   .3591908    -0.95   0.345    -1.044178    .3652243
       3120  |  -1.002319   .4592633    -2.18   0.029    -1.903354   -.1012846
       3135  |  -.9299459   .4184436    -2.22   0.026    -1.750896   -.1089958
       3136  |  -.4229061   .4696934    -0.90   0.368    -1.344404    .4985918
       3137  |  -.7056948   .4189136    -1.68   0.092    -1.527567    .1161775
       3215  |  -.5525533   .3746596    -1.47   0.141    -1.287603    .1824963
       3216  |  -.4743355   .3772556    -1.26   0.209    -1.214478    .2658075
       3217  |  -.6918483   .3751788    -1.84   0.065    -1.427917    .0442201
       3220  |  -.7892591   .4184781    -1.89   0.060    -1.610277    .0317588
       3235  |  -.8223519   .3840351    -2.14   0.032    -1.575796   -.0689083
       3236  |  -.8762598   .4222865    -2.08   0.038    -1.704749   -.0477702
       3237  |  -.9120185   .4695856    -1.94   0.052    -1.833305    .0092679
       3315  |   -.513714    .436807    -1.18   0.240    -1.370692    .3432636
       3316  |   -.296778   .4700271    -0.63   0.528     -1.21893    .6253746
       3317  |  -.7141344    .437899    -1.63   0.103    -1.573254    .1449857
       3320  |  -1.485532   .5197452    -2.86   0.004    -2.505227   -.4658366
       3330  |  -.8289675   .4151512    -2.00   0.046    -1.643458   -.0144768
             |
       _cons |  -.0202887    .357583    -0.06   0.955    -.7218356    .6812583
------------------------------------------------------------------------------

. lincom c.YH_gunN

 ( 1)  YH_gunN = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.078256   .0978758    11.02   0.000     .8862318    1.270279
------------------------------------------------------------------------------

. matrix EV2[1,1] = r(estimate)

. matrix EV2[1,2] = r(se)

. regress don_C_gunST c.YH_gun2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,260
-------------+----------------------------------   F(41, 1218)     =      9.03
       Model |  321.580751        41  7.84343294   Prob > F        =    0.0000
    Residual |  1057.38716     1,218  .868133954   R-squared       =    0.2332
-------------+----------------------------------   Adj R-squared   =    0.2074
       Total |  1378.96791     1,259  1.09528825   Root MSE        =    .93174

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    YH_gun2N |   1.095523   .0994431    11.02   0.000     .9004238    1.290621
             |
       block |
       1120  |  -.8536684   .4594059    -1.86   0.063    -1.754983    .0476464
       1130  |  -.9865374   .4695526    -2.10   0.036    -1.907759   -.0653157
       1210  |   -.763622   .3867229    -1.97   0.049    -1.522339    -.004905
       1221  |   -.799532   .4155293    -1.92   0.055    -1.614764    .0157006
       1222  |  -.6095994   .3937703    -1.55   0.122    -1.382143    .1629438
       1223  |  -.7767116   .3877689    -2.00   0.045    -1.537481   -.0159426
       1231  |  -1.134967   .5849274    -1.94   0.053    -2.282544    .0126106
       1232  |  -.5921974   .4506568    -1.31   0.189    -1.476347    .2919523
       1233  |  -.7318895   .3951893    -1.85   0.064    -1.507217    .0434377
       1311  |  -1.134046   .4695971    -2.41   0.016    -2.055355   -.2127374
       1312  |  -.6176811   .4184351    -1.48   0.140    -1.438615    .2032524
       1313  |  -.9649417   .4023371    -2.40   0.017    -1.754292    -.175591
       1321  |  -.7999848    .361019    -2.22   0.027    -1.508273   -.0916968
       1322  |  -.7026623   .3692049    -1.90   0.057     -1.42701    .0216858
       1323  |  -.9364108    .369801    -2.53   0.011    -1.661928   -.2108932
       1331  |  -.7486082   .4368704    -1.71   0.087     -1.60571    .1084938
       1332  |  -.8888877   .4222757    -2.10   0.035    -1.717356   -.0604192
       1333  |  -.8700219   .4044051    -2.15   0.032     -1.66343   -.0766141
       2010  |   -.574125   .5184719    -1.11   0.268    -1.591322    .4430721
       2020  |  -.3428842   .6430494    -0.53   0.594    -1.604492    .9187231
       2030  |  -.8865955   .4314586    -2.05   0.040     -1.73308   -.0401109
       3115  |  -.1932237   .3729006    -0.52   0.604    -.9248224     .538375
       3116  |  -.3825027   .3739572    -1.02   0.307    -1.116174     .351169
       3117  |  -.3394769   .3591908    -0.95   0.345    -1.044178    .3652243
       3120  |  -1.002319   .4592633    -2.18   0.029    -1.903354   -.1012846
       3135  |  -.9299459   .4184436    -2.22   0.026    -1.750896   -.1089958
       3136  |  -.4229061   .4696934    -0.90   0.368    -1.344404    .4985917
       3137  |  -.7056948   .4189136    -1.68   0.092    -1.527567    .1161775
       3215  |  -.5525533   .3746596    -1.47   0.141    -1.287603    .1824963
       3216  |  -.4743355   .3772556    -1.26   0.209    -1.214478    .2658075
       3217  |  -.6918483   .3751788    -1.84   0.065    -1.427917    .0442201
       3220  |  -.7892591   .4184781    -1.89   0.060    -1.610277    .0317588
       3235  |  -.8223519   .3840351    -2.14   0.032    -1.575796   -.0689083
       3236  |  -.8762598   .4222865    -2.08   0.038    -1.704749   -.0477702
       3237  |  -.9120185   .4695856    -1.94   0.052    -1.833305    .0092678
       3315  |   -.513714    .436807    -1.18   0.240    -1.370692    .3432636
       3316  |   -.296778   .4700271    -0.63   0.528     -1.21893    .6253746
       3317  |  -.7141344    .437899    -1.63   0.103    -1.573254    .1449856
       3320  |  -1.485532   .5197452    -2.86   0.004    -2.505227   -.4658366
       3330  |  -.8289675   .4151512    -2.00   0.046    -1.643458   -.0144768
             |
       _cons |  -.0259703   .3576728    -0.07   0.942    -.7276935    .6757529
------------------------------------------------------------------------------

. lincom c.YH_gun2N

 ( 1)  YH_gun2N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.095523   .0994431    11.02   0.000     .9004238    1.290621
------------------------------------------------------------------------------

. matrix EV2[1,3] = r(estimate)

. matrix EV2[1,4] = r(se)

. regress don_C_gunST c.votes_gunw1LPP3N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,260
-------------+----------------------------------   F(41, 1218)     =      9.02
       Model |  321.120136        41  7.83219843   Prob > F        =    0.0000
    Residual |  1057.84777     1,218  .868512127   R-squared       =    0.2329
-------------+----------------------------------   Adj R-squared   =    0.2070
       Total |  1378.96791     1,259  1.09528825   Root MSE        =    .93194

----------------------------------------------------------------------------------
     don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
votes_gunw1LPP3N |    1.08975   .0991577    10.99   0.000     .8952116    1.284289
                 |
           block |
           1120  |  -.8514871   .4595135    -1.85   0.064    -1.753013    .0500387
           1130  |  -.9860279   .4696548    -2.10   0.036     -1.90745   -.0646058
           1210  |  -.7652728   .3868077    -1.98   0.048    -1.524156   -.0063895
           1221  |  -.7975682   .4156308    -1.92   0.055       -1.613    .0178635
           1222  |  -.6195068     .39387    -1.57   0.116    -1.392246    .1532322
           1223  |  -.7791625     .38785    -2.01   0.045    -1.540091   -.0182342
           1231  |  -1.123247   .5849996    -1.92   0.055    -2.270966    .0244712
           1232  |  -.6045191   .4507264    -1.34   0.180    -1.488805    .2797673
           1233  |  -.7347075   .3952764    -1.86   0.063    -1.510206    .0407907
           1311  |  -1.137103   .4696958    -2.42   0.016    -2.058606   -.2156007
           1312  |  -.6278175   .4185241    -1.50   0.134    -1.448926    .1932905
           1313  |  -.9686773   .4024166    -2.41   0.016    -1.758184   -.1791708
           1321  |  -.7979557   .3611168    -2.21   0.027    -1.506436   -.0894757
           1322  |  -.7074201   .3692644    -1.92   0.056    -1.431885    .0170448
           1323  |  -.9336602   .3699063    -2.52   0.012    -1.659384    -.207936
           1331  |  -.7552097   .4369559    -1.73   0.084    -1.612479      .10206
           1332  |  -.8963164   .4223584    -2.12   0.034    -1.724947   -.0676857
           1333  |  -.8843456   .4044698    -2.19   0.029     -1.67788   -.0908108
           2010  |  -.5676104   .5185739    -1.09   0.274    -1.585008    .4497868
           2020  |  -.3557144   .6431713    -0.55   0.580    -1.617561    .9061321
           2030  |   -.894399   .4315352    -2.07   0.038    -1.741034   -.0477643
           3115  |   -.193479   .3729848    -0.52   0.604    -.9252429     .538285
           3116  |  -.3831596   .3740434    -1.02   0.306    -1.117001    .3506813
           3117  |  -.3381704   .3592647    -0.94   0.347    -1.043017    .3666758
           3120  |  -.9953823   .4593772    -2.17   0.030    -1.896641   -.0941238
           3135  |  -.9335165   .4185372    -2.23   0.026     -1.75465   -.1123826
           3136  |  -.4202032   .4697903    -0.89   0.371    -1.341891    .5014848
           3137  |  -.6988916   .4189777    -1.67   0.096     -1.52089    .1231064
           3215  |  -.5545785    .374751    -1.48   0.139    -1.289808    .1806505
           3216  |  -.4738419   .3773373    -1.26   0.209    -1.214145    .2664613
           3217  |  -.6861466   .3752367    -1.83   0.068    -1.422329    .0500354
           3220  |  -.7966692     .41858    -1.90   0.057    -1.617887    .0245485
           3235  |  -.8236129    .384119    -2.14   0.032    -1.577221   -.0700046
           3236  |  -.8832904   .4223898    -2.09   0.037    -1.711983   -.0545981
           3237  |  -.9214501   .4696784    -1.96   0.050    -1.842919    .0000183
           3315  |  -.5150062   .4369025    -1.18   0.239    -1.372171    .3421587
           3316  |  -.2852722   .4700855    -0.61   0.544    -1.207539    .6369949
           3317  |  -.7046667   .4379391    -1.61   0.108    -1.563865    .1545319
           3320  |  -1.469246   .5199745    -2.83   0.005    -2.489391    -.449101
           3330  |  -.8323157   .4152497    -2.00   0.045       -1.647   -.0176317
                 |
           _cons |  -.0479741   .3581309    -0.13   0.893    -.7505961    .6546479
----------------------------------------------------------------------------------

. lincom c.votes_gunw1LPP3N 

 ( 1)  votes_gunw1LPP3N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    1.08975   .0991577    10.99   0.000     .8952116    1.284289
------------------------------------------------------------------------------

. matrix EV2[1,5] = r(estimate)

. matrix EV2[1,6] = r(se)

. 
. 
. regress don_C_wall_inST c.YH_wall_inN i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,261
-------------+----------------------------------   F(41, 1219)     =      5.89
       Model |  221.688748        41  5.40704264   Prob > F        =    0.0000
    Residual |  1119.30918     1,219   .91821918   R-squared       =    0.1653
-------------+----------------------------------   Adj R-squared   =    0.1372
       Total |  1340.99793     1,260  1.06428407   Root MSE        =    .95824

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 YH_wall_inN |   .6214078   .1197462     5.19   0.000     .3864763    .8563392
             |
       block |
       1120  |  -.2709125   .4751751    -0.57   0.569    -1.203164    .6613391
       1130  |   -.474877   .4842356    -0.98   0.327    -1.424905    .4751506
       1210  |  -.3429695   .3977265    -0.86   0.389    -1.123274     .437335
       1221  |  -.7571391   .4291415    -1.76   0.078    -1.599077    .0847987
       1222  |  -.5118712   .4099169    -1.25   0.212    -1.316092    .2923497
       1223  |  -.7486499   .4019921    -1.86   0.063    -1.537323    .0400233
       1231  |   .1129776   .6011936     0.19   0.851    -1.066511    1.292466
       1232  |  -.2728563   .4648009    -0.59   0.557    -1.184755     .639042
       1233  |  -.3808538   .4081873    -0.93   0.351    -1.181681    .4199737
       1311  |  -.3306377   .4834437    -0.68   0.494    -1.279112    .6178362
       1312  |  -.5047283    .430494    -1.17   0.241     -1.34932    .3398629
       1313  |  -.3375258   .4149341    -0.81   0.416     -1.15159    .4765384
       1321  |  -.6836998   .3762225    -1.82   0.069    -1.421815    .0544157
       1322  |   -.466672   .3836574    -1.22   0.224    -1.219374      .28603
       1323  |  -.7537947   .3855899    -1.95   0.051    -1.510288    .0026987
       1331  |  -.4872442   .4509595    -1.08   0.280    -1.371987    .3974986
       1332  |  -.3318266   .4365959    -0.76   0.447    -1.188389    .5247361
       1333  |  -.4391696    .417351    -1.05   0.293    -1.257976    .3796363
       2010  |   .1013349   .5332765     0.19   0.849    -.9449067    1.147576
       2020  |  -.2294801   .6664533    -0.34   0.731    -1.537003    1.078043
       2030  |  -.1376943   .4436314    -0.31   0.756     -1.00806    .7326715
       3115  |  -.1346789   .3835294    -0.35   0.726    -.8871298     .617772
       3116  |  -.1159876   .3843249    -0.30   0.763    -.8699992     .638024
       3117  |  -.1619239   .3692361    -0.44   0.661    -.8863327    .5624848
       3120  |  -.4827288   .4758786    -1.01   0.311    -1.416361    .4509032
       3135  |  -.0733559   .4303363    -0.17   0.865    -.9176378    .7709259
       3136  |  -.4327972   .4829163    -0.90   0.370    -1.380236     .514642
       3137  |  -.4624215   .4303337    -1.07   0.283    -1.306698    .3818553
       3215  |  -.0868864   .3850277    -0.23   0.822    -.8422769     .668504
       3216  |  -.3748665    .387764    -0.97   0.334    -1.135625    .3858922
       3217  |   -.338438   .3855385    -0.88   0.380    -1.094831    .4179546
       3220  |  -.6831821   .4330199    -1.58   0.115    -1.532729    .1663649
       3235  |  -.2894093   .3950951    -0.73   0.464    -1.064551    .4857324
       3236  |  -.5045511   .4347165    -1.16   0.246    -1.357427    .3483243
       3237  |  -.0636366   .4832612    -0.13   0.895    -1.011753    .8844794
       3315  |   .1006811   .4492475     0.22   0.823     -.780703    .9820651
       3316  |  -.2045239   .4829835    -0.42   0.672    -1.152095    .7430471
       3317  |  -.7505398   .4492298    -1.67   0.095    -1.631889    .1308094
       3320  |   -.217775   .5362101    -0.41   0.685    -1.269772     .834222
       3330  |  -.4165118   .4268785    -0.98   0.329     -1.25401    .4209863
             |
       _cons |    .039294   .3726813     0.11   0.916    -.6918739    .7704619
------------------------------------------------------------------------------

. lincom c.YH_wall_inN

 ( 1)  YH_wall_inN = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6214078   .1197462     5.19   0.000     .3864763    .8563392
------------------------------------------------------------------------------

. matrix EV2[2,1] = r(estimate)

. matrix EV2[2,2] = r(se)

. regress don_C_wall_inST c.YH_wall_in2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,261
-------------+----------------------------------   F(41, 1219)     =      5.89
       Model |  221.688748        41  5.40704263   Prob > F        =    0.0000
    Residual |  1119.30918     1,219   .91821918   R-squared       =    0.1653
-------------+----------------------------------   Adj R-squared   =    0.1372
       Total |  1340.99793     1,260  1.06428407   Root MSE        =    .95824

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
YH_wall_in2N |   .6424546   .1238019     5.19   0.000     .3995661     .885343
             |
       block |
       1120  |  -.2709125   .4751751    -0.57   0.569    -1.203164    .6613391
       1130  |   -.474877   .4842356    -0.98   0.327    -1.424905    .4751506
       1210  |  -.3429695   .3977265    -0.86   0.389    -1.123274     .437335
       1221  |  -.7571391   .4291415    -1.76   0.078    -1.599077    .0847987
       1222  |  -.5118712   .4099169    -1.25   0.212    -1.316092    .2923497
       1223  |  -.7486499   .4019921    -1.86   0.063    -1.537323    .0400233
       1231  |   .1129776   .6011936     0.19   0.851    -1.066511    1.292466
       1232  |  -.2728563   .4648009    -0.59   0.557    -1.184755     .639042
       1233  |  -.3808538   .4081873    -0.93   0.351    -1.181681    .4199737
       1311  |  -.3306377   .4834437    -0.68   0.494    -1.279112    .6178362
       1312  |  -.5047283    .430494    -1.17   0.241     -1.34932    .3398629
       1313  |  -.3375258   .4149341    -0.81   0.416     -1.15159    .4765384
       1321  |  -.6836998   .3762225    -1.82   0.069    -1.421815    .0544157
       1322  |   -.466672   .3836574    -1.22   0.224    -1.219374      .28603
       1323  |  -.7537947   .3855899    -1.95   0.051    -1.510288    .0026987
       1331  |  -.4872442   .4509595    -1.08   0.280    -1.371987    .3974986
       1332  |  -.3318266   .4365959    -0.76   0.447    -1.188389    .5247361
       1333  |  -.4391696    .417351    -1.05   0.293    -1.257976    .3796363
       2010  |   .1013349   .5332765     0.19   0.849    -.9449067    1.147577
       2020  |  -.2294801   .6664533    -0.34   0.731    -1.537003    1.078043
       2030  |  -.1376943   .4436314    -0.31   0.756     -1.00806    .7326715
       3115  |  -.1346789   .3835294    -0.35   0.726    -.8871298     .617772
       3116  |  -.1159876   .3843249    -0.30   0.763    -.8699992     .638024
       3117  |  -.1619239   .3692361    -0.44   0.661    -.8863327    .5624848
       3120  |  -.4827288   .4758786    -1.01   0.311    -1.416361    .4509032
       3135  |  -.0733559   .4303363    -0.17   0.865    -.9176378    .7709259
       3136  |  -.4327972   .4829163    -0.90   0.370    -1.380236     .514642
       3137  |  -.4624215   .4303337    -1.07   0.283    -1.306698    .3818553
       3215  |  -.0868864   .3850277    -0.23   0.822    -.8422768     .668504
       3216  |  -.3748665    .387764    -0.97   0.334    -1.135625    .3858922
       3217  |   -.338438   .3855385    -0.88   0.380    -1.094831    .4179546
       3220  |  -.6831821   .4330199    -1.58   0.115    -1.532729    .1663649
       3235  |  -.2894093   .3950951    -0.73   0.464    -1.064551    .4857324
       3236  |  -.5045511   .4347165    -1.16   0.246    -1.357427    .3483244
       3237  |  -.0636366   .4832612    -0.13   0.895    -1.011753    .8844794
       3315  |   .1006811   .4492475     0.22   0.823     -.780703    .9820652
       3316  |  -.2045239   .4829835    -0.42   0.672    -1.152095    .7430471
       3317  |  -.7505398   .4492298    -1.67   0.095    -1.631889    .1308094
       3320  |   -.217775   .5362101    -0.41   0.685    -1.269772     .834222
       3330  |  -.4165118   .4268785    -0.98   0.329     -1.25401    .4209863
             |
       _cons |   .0218551   .3734877     0.06   0.953    -.7108949    .7546051
------------------------------------------------------------------------------

. lincom c.YH_wall_in2N 

 ( 1)  YH_wall_in2N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6424546   .1238019     5.19   0.000     .3995661     .885343
------------------------------------------------------------------------------

. matrix EV2[2,3] = r(estimate)

. matrix EV2[2,4] = r(se)

. regress don_C_wall_inST c.votes_wall_inw1LPP3N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,261
-------------+----------------------------------   F(41, 1219)     =      5.88
       Model |  221.463093        41  5.40153886   Prob > F        =    0.0000
    Residual |  1119.53483     1,219  .918404294   R-squared       =    0.1651
-------------+----------------------------------   Adj R-squared   =    0.1371
       Total |  1340.99793     1,260  1.06428407   Root MSE        =    .95833

--------------------------------------------------------------------------------------
     don_C_wall_inST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
votes_wall_inw1LPP3N |     .65748   .1272922     5.17   0.000     .4077438    .9072161
                     |
               block |
               1120  |  -.2794657   .4750678    -0.59   0.556    -1.211507    .6525756
               1130  |  -.4640438   .4844571    -0.96   0.338    -1.414506    .4864184
               1210  |  -.3363882   .3977765    -0.85   0.398    -1.116791    .4440142
               1221  |   -.755988   .4292704    -1.76   0.078    -1.598179    .0862028
               1222  |  -.5131757   .4099645    -1.25   0.211     -1.31749    .2911386
               1223  |  -.7507681   .4020104    -1.87   0.062    -1.539477    .0379409
               1231  |   .1215823   .6013358     0.20   0.840    -1.058186     1.30135
               1232  |  -.2737233    .464848    -0.59   0.556    -1.185714    .6382676
               1233  |    -.38132   .4082362    -0.93   0.350    -1.182244    .4196034
               1311  |  -.3396562   .4834178    -0.70   0.482    -1.288079    .6087669
               1312  |  -.4866787   .4306488    -1.13   0.259    -1.331574    .3582163
               1313  |  -.3323669   .4150683    -0.80   0.423    -1.146694    .4819606
               1321  |  -.6894555   .3761174    -1.83   0.067    -1.427365    .0484537
               1322  |  -.4710541    .383608    -1.23   0.220    -1.223659     .281551
               1323  |  -.7586042   .3855184    -1.97   0.049    -1.514957   -.0022509
               1331  |  -.4972447   .4508547    -1.10   0.270    -1.381782    .3872926
               1332  |  -.3411495   .4364771    -0.78   0.435    -1.197479    .5151801
               1333  |  -.4434017   .4173376    -1.06   0.288    -1.262181    .3753779
               2010  |   .1118402   .5333861     0.21   0.834    -.9346163    1.158297
               2020  |  -.2351434   .6664317    -0.35   0.724    -1.542624    1.072337
               2030  |  -.1312302    .443698    -0.30   0.767    -1.001727    .7392662
               3115  |  -.1244771   .3834796    -0.32   0.746    -.8768303    .6278761
               3116  |  -.0999902   .3842837    -0.26   0.795     -.853921    .6539407
               3117  |  -.1539266   .3691993    -0.42   0.677    -.8782632    .5704099
               3120  |  -.4894871   .4757993    -1.03   0.304    -1.422963    .4439893
               3135  |  -.0871557   .4303973    -0.20   0.840    -.9315572    .7572459
               3136  |  -.4297553   .4829692    -0.89   0.374    -1.377298    .5177877
               3137  |  -.4573529   .4303778    -1.06   0.288    -1.301716    .3870105
               3215  |  -.0756611   .3850267    -0.20   0.844    -.8310495    .6797273
               3216  |  -.3687032   .3877822    -0.95   0.342    -1.129498    .3920913
               3217  |  -.3303074   .3855336    -0.86   0.392     -1.08669    .4260755
               3220  |  -.6942257   .4328546    -1.60   0.109    -1.543448    .1549969
               3235  |  -.2797577   .3951903    -0.71   0.479    -1.055086    .4955708
               3236  |  -.5023221   .4347852    -1.16   0.248    -1.355332    .3506882
               3237  |  -.0480599   .4834387    -0.10   0.921     -.996524    .9004042
               3315  |   .1162396   .4492754     0.26   0.796    -.7651991    .9976783
               3316  |  -.1990368    .483015    -0.41   0.680     -1.14667     .748596
               3317  |   -.751244   .4492754    -1.67   0.095    -1.632683    .1301947
               3320  |  -.2147534    .536356    -0.40   0.689    -1.267037    .8375298
               3330  |  -.4090569   .4269456    -0.96   0.338    -1.246687    .4285728
                     |
               _cons |   .0255391   .3734536     0.07   0.945     -.707144    .7582223
--------------------------------------------------------------------------------------

. lincom c.votes_wall_inw1LPP3N 

 ( 1)  votes_wall_inw1LPP3N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |     .65748   .1272922     5.17   0.000     .4077438    .9072161
------------------------------------------------------------------------------

. matrix EV2[2,5] = r(estimate)

. matrix EV2[2,6] = r(se)

. 
. regress writing_minWNST c.YH_minWN i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       542
-------------+----------------------------------   F(41, 500)      =      1.02
       Model |  57.2528037        41  1.39640985   Prob > F        =    0.4432
    Residual |  685.860492       500  1.37172098   R-squared       =    0.0770
-------------+----------------------------------   Adj R-squared   =    0.0014
       Total |  743.113296       541  1.37359204   Root MSE        =    1.1712

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    YH_minWN |   .4446054   .1783333     2.49   0.013     .0942304    .7949805
             |
       block |
       1120  |  -.4156803   .8284065    -0.50   0.616    -2.043267    1.211906
       1130  |  -.5441573   1.070868    -0.51   0.612    -2.648113    1.559798
       1210  |   .0301981   .7502087     0.04   0.968    -1.443752    1.504148
       1221  |  -.2663229   .8553351    -0.31   0.756    -1.946817    1.414171
       1222  |   .0351789   .7453461     0.05   0.962    -1.429217    1.499575
       1223  |   .4650888   .7502664     0.62   0.536    -1.008974    1.939152
       1231  |  -.4163903   1.069199    -0.39   0.697    -2.517066    1.684286
       1232  |  -.1040756   .8951015    -0.12   0.907    -1.862699    1.654548
       1233  |  -.0876637   .7408386    -0.12   0.906    -1.543204    1.367877
       1311  |  -.1313377    .895591    -0.15   0.883    -1.890923    1.628248
       1312  |  -.1945321   .8086633    -0.24   0.810    -1.783329    1.394265
       1313  |  -.1089205   .7631104    -0.14   0.887    -1.608219    1.390378
       1321  |  -.2565995    .692935    -0.37   0.711    -1.618023    1.104824
       1322  |   .3219989   .7024349     0.46   0.647    -1.058089    1.702087
       1323  |   .0263139   .7025177     0.04   0.970    -1.353937    1.406564
       1331  |   .1961502   .7713902     0.25   0.799    -1.319415    1.711716
       1332  |   .1464276   .7940732     0.18   0.854    -1.413704    1.706559
       1333  |  -.0555129   .7808481    -0.07   0.943    -1.589661    1.478635
       2010  |  -.4935442   1.069913    -0.46   0.645    -2.595624    1.608536
       2020  |   2.849686   1.069862     2.66   0.008     .7477075    4.951665
       2030  |   .2248493   .8553877     0.26   0.793    -1.455748    1.905447
       3115  |   .3864775   .7115453     0.54   0.587     -1.01151    1.784465
       3116  |  -.0262969    .707439    -0.04   0.970    -1.416216    1.363623
       3117  |    .011507    .691634     0.02   0.987     -1.34736    1.370374
       3120  |   1.112539    .958195     1.16   0.246    -.7700456    2.995124
       3135  |   .0789998   .8297277     0.10   0.924    -1.551183    1.709182
       3136  |  -.3194083    .896699    -0.36   0.722    -2.081171    1.442354
       3137  |   .0984869   .7955206     0.12   0.902    -1.464488    1.661462
       3215  |   .0459386    .718976     0.06   0.949    -1.366648    1.458525
       3216  |  -.1111819   .7254984    -0.15   0.878    -1.536583    1.314219
       3217  |   .0695061   .7213429     0.10   0.923    -1.347731    1.486743
       3220  |  -.0124859   .8084249    -0.02   0.988    -1.600814    1.575843
       3235  |  -.2515963   .7407518    -0.34   0.734    -1.706966    1.203773
       3236  |   .0523446    .896434     0.06   0.953    -1.708897    1.813586
       3237  |  -.4098778   .9580333    -0.43   0.669    -2.292145    1.472389
       3315  |    .259901   .8083126     0.32   0.748    -1.328207    1.848009
       3316  |  -.0332651   .8553673    -0.04   0.969    -1.713822    1.647292
       3317  |   .1928827   .8563356     0.23   0.822    -1.489577    1.875342
       3320  |   .0220336   .9563277     0.02   0.982    -1.856882     1.90095
       3330  |    1.11724    .828837     1.35   0.178    -.5111923    2.745673
             |
       _cons |  -.3257245   .6850846    -0.48   0.635    -1.671724    1.020275
------------------------------------------------------------------------------

. lincom c.YH_minWN

 ( 1)  YH_minWN = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4446054   .1783333     2.49   0.013     .0942304    .7949805
------------------------------------------------------------------------------

. matrix EV2[3,1] = r(estimate)

. matrix EV2[3,2] = r(se)

. regress writing_minWNST c.YH_minW2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       542
-------------+----------------------------------   F(41, 500)      =      1.02
       Model |  57.2528039        41  1.39640985   Prob > F        =    0.4432
    Residual |  685.860492       500  1.37172098   R-squared       =    0.0770
-------------+----------------------------------   Adj R-squared   =    0.0014
       Total |  743.113296       541  1.37359204   Root MSE        =    1.1712

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   YH_minW2N |   .7361902   .2952893     2.49   0.013     .1560294    1.316351
             |
       block |
       1120  |  -.4156803   .8284065    -0.50   0.616    -2.043267    1.211906
       1130  |  -.5441573   1.070868    -0.51   0.612    -2.648113    1.559798
       1210  |   .0301981   .7502087     0.04   0.968    -1.443752    1.504148
       1221  |  -.2663229   .8553351    -0.31   0.756    -1.946817    1.414171
       1222  |   .0351789   .7453461     0.05   0.962    -1.429217    1.499575
       1223  |   .4650888   .7502664     0.62   0.536    -1.008974    1.939152
       1231  |  -.4163903   1.069199    -0.39   0.697    -2.517066    1.684286
       1232  |  -.1040756   .8951015    -0.12   0.907    -1.862699    1.654548
       1233  |  -.0876637   .7408386    -0.12   0.906    -1.543204    1.367877
       1311  |  -.1313377    .895591    -0.15   0.883    -1.890923    1.628248
       1312  |  -.1945321   .8086633    -0.24   0.810    -1.783329    1.394265
       1313  |  -.1089205   .7631104    -0.14   0.887    -1.608219    1.390378
       1321  |  -.2565995    .692935    -0.37   0.711    -1.618023    1.104824
       1322  |   .3219989   .7024349     0.46   0.647    -1.058089    1.702087
       1323  |   .0263139   .7025177     0.04   0.970    -1.353937    1.406564
       1331  |   .1961502   .7713902     0.25   0.799    -1.319415    1.711716
       1332  |   .1464276   .7940732     0.18   0.854    -1.413704    1.706559
       1333  |  -.0555129   .7808481    -0.07   0.943    -1.589661    1.478635
       2010  |  -.4935442   1.069913    -0.46   0.645    -2.595624    1.608536
       2020  |   2.849686   1.069862     2.66   0.008     .7477075    4.951665
       2030  |   .2248493   .8553877     0.26   0.793    -1.455748    1.905447
       3115  |   .3864775   .7115453     0.54   0.587     -1.01151    1.784465
       3116  |  -.0262969    .707439    -0.04   0.970    -1.416216    1.363623
       3117  |    .011507    .691634     0.02   0.987     -1.34736    1.370374
       3120  |   1.112539    .958195     1.16   0.246    -.7700456    2.995124
       3135  |   .0789997   .8297277     0.10   0.924    -1.551183    1.709182
       3136  |  -.3194083    .896699    -0.36   0.722    -2.081171    1.442354
       3137  |   .0984869   .7955206     0.12   0.902    -1.464488    1.661462
       3215  |   .0459385    .718976     0.06   0.949    -1.366648    1.458525
       3216  |  -.1111819   .7254984    -0.15   0.878    -1.536583    1.314219
       3217  |   .0695061   .7213429     0.10   0.923    -1.347731    1.486743
       3220  |  -.0124859   .8084249    -0.02   0.988    -1.600814    1.575843
       3235  |  -.2515963   .7407518    -0.34   0.734    -1.706966    1.203773
       3236  |   .0523446    .896434     0.06   0.953    -1.708897    1.813586
       3237  |  -.4098778   .9580333    -0.43   0.669    -2.292145    1.472389
       3315  |    .259901   .8083126     0.32   0.748    -1.328207    1.848009
       3316  |  -.0332651   .8553673    -0.04   0.969    -1.713822    1.647292
       3317  |   .1928827   .8563356     0.23   0.822    -1.489577    1.875342
       3320  |   .0220336   .9563277     0.02   0.982    -1.856882     1.90095
       3330  |    1.11724    .828837     1.35   0.178    -.5111923    2.745673
             |
       _cons |  -.3288013   .6852839    -0.48   0.632    -1.675192    1.017589
------------------------------------------------------------------------------

. lincom c.YH_minW2N

 ( 1)  YH_minW2N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7361902   .2952893     2.49   0.013     .1560294    1.316351
------------------------------------------------------------------------------

. matrix EV2[3,3] = r(estimate)

. matrix EV2[3,4] = r(se)

. regress writing_minWNST c.abs_votes_minWw1LPP3N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       542
-------------+----------------------------------   F(41, 500)      =      0.99
       Model |  55.6148127        41  1.35645885   Prob > F        =    0.4975
    Residual |  687.498483       500  1.37499697   R-squared       =    0.0748
-------------+----------------------------------   Adj R-squared   =   -0.0010
       Total |  743.113296       541  1.37359204   Root MSE        =    1.1726

---------------------------------------------------------------------------------------
      writing_minWNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
abs_votes_minWw1LPP3N |   .3511955   .1569097     2.24   0.026     .0429118    .6594791
                      |
                block |
                1120  |  -.3933296   .8292451    -0.47   0.635    -2.022564    1.235905
                1130  |  -.5104546   1.071712    -0.48   0.634    -2.616068    1.595159
                1210  |   .0470217   .7510672     0.06   0.950    -1.428615    1.522658
                1221  |  -.2477573   .8563581    -0.29   0.772    -1.930261    1.434746
                1222  |   .0379987   .7462542     0.05   0.959    -1.428182    1.504179
                1223  |   .4587619   .7511396     0.61   0.542    -1.017017    1.934541
                1231  |  -.3699764   1.070486    -0.35   0.730    -2.473181    1.733229
                1232  |   -.058946   .8957275    -0.07   0.948      -1.8188    1.700908
                1233  |  -.0630213   .7416243    -0.08   0.932    -1.520105    1.394063
                1311  |  -.0691316   .8958344    -0.08   0.939    -1.829195    1.690932
                1312  |  -.1752004    .810105    -0.22   0.829     -1.76683    1.416429
                1313  |  -.0997994   .7639755    -0.13   0.896    -1.600797    1.401198
                1321  |  -.2347369   .6937225    -0.34   0.735    -1.597707    1.128233
                1322  |   .3567543   .7032424     0.51   0.612     -1.02492    1.738429
                1323  |   .0584756   .7032195     0.08   0.934    -1.323154    1.440105
                1331  |   .2306491   .7720036     0.30   0.765    -1.286122     1.74742
                1332  |   .1270871   .7948243     0.16   0.873     -1.43452    1.688694
                1333  |  -.0529667   .7818051    -0.07   0.946    -1.588995    1.483061
                2010  |  -.4636285   1.070895    -0.43   0.665    -2.567637     1.64038
                2020  |   2.864419   1.071061     2.67   0.008     .7600849    4.968754
                2030  |   .2595933   .8563581     0.30   0.762     -1.42291    1.942097
                3115  |   .4026063   .7123502     0.57   0.572    -.9969623    1.802175
                3116  |  -.0087696    .708153    -0.01   0.990    -1.400092    1.382553
                3117  |   .0478731   .6918922     0.07   0.945    -1.311501    1.407247
                3120  |   1.146242   .9588536     1.20   0.232    -.7376367    3.030121
                3135  |   .0848524   .8309149     0.10   0.919    -1.547663    1.717367
                3136  |  -.2983682   .8976076    -0.33   0.740    -2.061916    1.465179
                3137  |   .1068642   .7967623     0.13   0.893     -1.45855    1.672279
                3215  |    .066933   .7198042     0.09   0.926    -1.347281    1.481147
                3216  |  -.0741442   .7260587    -0.10   0.919    -1.500646    1.352358
                3217  |   .0949512   .7219376     0.13   0.895    -1.323454    1.513356
                3220  |  -.0026093   .8093397    -0.00   0.997    -1.592735    1.587516
                3235  |  -.2530909   .7416351    -0.34   0.733    -1.710196    1.204014
                3236  |   .0987101   .8966928     0.11   0.912     -1.66304     1.86046
                3237  |  -.3592914   .9583399    -0.37   0.708    -2.242161    1.523578
                3315  |   .2889453   .8091741     0.36   0.721    -1.300855    1.878746
                3316  |  -.0258433   .8564399    -0.03   0.976    -1.708508    1.656821
                3317  |   .2215398   .8570019     0.26   0.796    -1.462229    1.905308
                3320  |   .0211981   .9574832     0.02   0.982    -1.859988    1.902384
                3330  |   1.112211   .8298881     1.34   0.181    -.5182862    2.742709
                      |
                _cons |  -.2153664   .6809509    -0.32   0.752    -1.553244    1.122511
---------------------------------------------------------------------------------------

. lincom c.abs_votes_minWw1LPP3N 

 ( 1)  abs_votes_minWw1LPP3N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3511955   .1569097     2.24   0.026     .0429118    .6594791
------------------------------------------------------------------------------

. matrix EV2[3,5] = r(estimate)

. matrix EV2[3,6] = r(se)

. 
. 
. regress writing_abortionNST c.YH_abortion_inN i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       541
-------------+----------------------------------   F(41, 499)      =      2.03
       Model |   85.143983        41  2.07668251   Prob > F        =    0.0003
    Residual |  509.711087       499   1.0214651   R-squared       =    0.1431
-------------+----------------------------------   Adj R-squared   =    0.0727
       Total |   594.85507       540  1.10158346   Root MSE        =    1.0107

---------------------------------------------------------------------------------
writing_abo~NST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
YH_abortion_inN |   .9536118   .1508332     6.32   0.000     .6572654    1.249958
                |
          block |
          1120  |  -.4805155   .7148315    -0.67   0.502    -1.884966    .9239349
          1130  |   -.608964   .9226271    -0.66   0.510    -2.421677    1.203749
          1210  |   .3997948      .6475     0.62   0.537    -.8723675    1.671957
          1221  |  -.2714372   .7385082    -0.37   0.713    -1.722406    1.179532
          1222  |   -.263401   .6430591    -0.41   0.682    -1.526838    1.000036
          1223  |   .1726114   .6479493     0.27   0.790    -1.100434    1.445657
          1231  |   .7592445   .9228973     0.82   0.411    -1.053999    2.572488
          1232  |   .4912476   .7720586     0.64   0.525    -1.025639    2.008134
          1233  |   .3940764   .6397813     0.62   0.538    -.8629206    1.651073
          1311  |   1.030107   .7719166     1.33   0.183    -.4864998    2.546715
          1312  |    .225143   .6974953     0.32   0.747    -1.145246    1.595532
          1313  |  -.0264551   .6582956    -0.04   0.968    -1.319828    1.266918
          1321  |   .1721746   .5980314     0.29   0.774    -1.002795    1.347144
          1322  |  -.0811981   .6062014    -0.13   0.893     -1.27222    1.109823
          1323  |   .2420927   .6063817     0.40   0.690    -.9492834    1.433469
          1331  |   .2411513   .6654855     0.36   0.717    -1.066348     1.54865
          1332  |   .0444375   .6842544     0.06   0.948    -1.299937    1.388812
          1333  |   .2098068   .6739136     0.31   0.756    -1.114251    1.533865
          2010  |   2.061033   .9226247     2.23   0.026      .248325    3.873741
          2020  |  -.5970637   .9226199    -0.65   0.518    -2.409762    1.215635
          2030  |  -.0200625   .7381316    -0.03   0.978    -1.470291    1.430166
          3115  |   .0868672   .6141173     0.14   0.888    -1.119707    1.293442
          3116  |   .3795519   .6102534     0.62   0.534    -.8194308    1.578535
          3117  |   .1425814   .5961945     0.24   0.811    -1.028779    1.313942
          3120  |  -.3450895   .8253306    -0.42   0.676    -1.966641    1.276462
          3135  |  -.3313315   .7146559    -0.46   0.643    -1.735437    1.072774
          3136  |  -.0963363   .7722837    -0.12   0.901    -1.613665    1.420992
          3137  |  -.2947206   .6843158    -0.43   0.667    -1.639216    1.049775
          3215  |  -.2256111   .6204181    -0.36   0.716    -1.444565    .9933424
          3216  |    .015833   .6260827     0.03   0.980     -1.21425    1.245916
          3217  |  -.0636466   .6223454    -0.10   0.919    -1.286387    1.159094
          3220  |  -.2026148   .6979143    -0.29   0.772    -1.573827    1.168598
          3235  |  -.1086762   .6395795    -0.17   0.865    -1.365277    1.147924
          3236  |  -.2500922   .7736862    -0.32   0.747    -1.770176    1.269992
          3237  |  -.3227891   .8253089    -0.39   0.696    -1.944298    1.298719
          3315  |   .0298228   .6987974     0.04   0.966    -1.343125    1.402771
          3316  |   .4673018   .7389761     0.63   0.527    -.9845863     1.91919
          3317  |   -.596997   .7380975    -0.81   0.419    -2.047159    .8531649
          3320  |  -.3474505   .8253246    -0.42   0.674     -1.96899    1.274089
          3330  |   .0680284    .715658     0.10   0.924    -1.338046    1.474103
                |
          _cons |  -.6107788     .59223    -1.03   0.303     -1.77435    .5527929
---------------------------------------------------------------------------------

. lincom c.YH_abortion_inN

 ( 1)  YH_abortion_inN = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9536118   .1508332     6.32   0.000     .6572654    1.249958
------------------------------------------------------------------------------

. matrix EV2[4,1] = r(estimate)

. matrix EV2[4,2] = r(se)

. regress writing_abortionNST c.YH_abortion_in2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       541
-------------+----------------------------------   F(41, 499)      =      2.03
       Model |  85.1439827        41  2.07668251   Prob > F        =    0.0003
    Residual |  509.711087       499   1.0214651   R-squared       =    0.1431
-------------+----------------------------------   Adj R-squared   =    0.0727
       Total |   594.85507       540  1.10158346   Root MSE        =    1.0107

----------------------------------------------------------------------------------
writing_abor~NST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
YH_abortion_in2N |   1.093162   .1729059     6.32   0.000     .7534487    1.432875
                 |
           block |
           1120  |  -.4805155   .7148315    -0.67   0.502    -1.884966    .9239349
           1130  |   -.608964   .9226271    -0.66   0.510    -2.421677    1.203749
           1210  |   .3997948      .6475     0.62   0.537    -.8723675    1.671957
           1221  |  -.2714372   .7385082    -0.37   0.713    -1.722406    1.179532
           1222  |   -.263401   .6430591    -0.41   0.682    -1.526838    1.000036
           1223  |   .1726114   .6479493     0.27   0.790    -1.100434    1.445657
           1231  |   .7592445   .9228973     0.82   0.411    -1.053999    2.572488
           1232  |   .4912476   .7720586     0.64   0.525    -1.025639    2.008134
           1233  |   .3940764   .6397813     0.62   0.538    -.8629206    1.651073
           1311  |   1.030107   .7719166     1.33   0.183    -.4864998    2.546715
           1312  |    .225143   .6974953     0.32   0.747    -1.145246    1.595532
           1313  |  -.0264551   .6582956    -0.04   0.968    -1.319828    1.266918
           1321  |   .1721746   .5980314     0.29   0.774    -1.002795    1.347144
           1322  |  -.0811981   .6062014    -0.13   0.893     -1.27222    1.109823
           1323  |   .2420927   .6063817     0.40   0.690    -.9492834    1.433469
           1331  |   .2411513   .6654855     0.36   0.717    -1.066348     1.54865
           1332  |   .0444375   .6842544     0.06   0.948    -1.299937    1.388812
           1333  |   .2098068   .6739136     0.31   0.756    -1.114251    1.533865
           2010  |   2.061033   .9226247     2.23   0.026     .2483249    3.873741
           2020  |  -.5970637   .9226199    -0.65   0.518    -2.409762    1.215635
           2030  |  -.0200625   .7381316    -0.03   0.978    -1.470291    1.430166
           3115  |   .0868672   .6141173     0.14   0.888    -1.119707    1.293442
           3116  |   .3795519   .6102534     0.62   0.534    -.8194308    1.578535
           3117  |   .1425814   .5961945     0.24   0.811    -1.028779    1.313942
           3120  |  -.3450895   .8253306    -0.42   0.676    -1.966641    1.276462
           3135  |  -.3313315   .7146559    -0.46   0.643    -1.735437    1.072774
           3136  |  -.0963363   .7722837    -0.12   0.901    -1.613665    1.420992
           3137  |  -.2947206   .6843158    -0.43   0.667    -1.639216    1.049775
           3215  |  -.2256111   .6204181    -0.36   0.716    -1.444565    .9933424
           3216  |    .015833   .6260827     0.03   0.980     -1.21425    1.245916
           3217  |  -.0636466   .6223454    -0.10   0.919    -1.286387    1.159094
           3220  |  -.2026148   .6979143    -0.29   0.772    -1.573827    1.168598
           3235  |  -.1086762   .6395795    -0.17   0.865    -1.365277    1.147924
           3236  |  -.2500922   .7736862    -0.32   0.747    -1.770176    1.269992
           3237  |  -.3227891   .8253089    -0.39   0.696    -1.944298    1.298719
           3315  |   .0298228   .6987974     0.04   0.966    -1.343125    1.402771
           3316  |   .4673018   .7389761     0.63   0.527    -.9845863     1.91919
           3317  |   -.596997   .7380975    -0.81   0.419    -2.047159    .8531649
           3320  |  -.3474505   .8253246    -0.42   0.674     -1.96899    1.274089
           3330  |   .0680284    .715658     0.10   0.924    -1.338046    1.474103
                 |
           _cons |   -.742918   .5961584    -1.25   0.213    -1.914208    .4283718
----------------------------------------------------------------------------------

. lincom c.YH_abortion_in2N 

 ( 1)  YH_abortion_in2N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.093162   .1729059     6.32   0.000     .7534487    1.432875
------------------------------------------------------------------------------

. matrix EV2[4,3] = r(estimate)

. matrix EV2[4,4] = r(se)

. regress writing_abortionNST c.abs_votes_abortion_inw1LPP3N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       541
-------------+----------------------------------   F(41, 499)      =      1.97
       Model |   82.732127        41  2.01785676   Prob > F        =    0.0005
    Residual |  512.122943       499  1.02629848   R-squared       =    0.1391
-------------+----------------------------------   Adj R-squared   =    0.0683
       Total |   594.85507       540  1.10158346   Root MSE        =    1.0131

----------------------------------------------------------------------------------------------
         writing_abortionNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------------------+----------------------------------------------------------------
abs_votes_abortion_inw1LPP3N |   .7820316   .1278193     6.12   0.000     .5309013    1.033162
                             |
                       block |
                       1120  |   -.433056   .7167511    -0.60   0.546    -1.841278     .975166
                       1130  |  -.5894623   .9247977    -0.64   0.524     -2.40644    1.227515
                       1210  |   .4775725   .6494456     0.74   0.462    -.7984124    1.753557
                       1221  |  -.1707616   .7410306    -0.23   0.818    -1.626686    1.285163
                       1222  |  -.2411372   .6446431    -0.37   0.709    -1.507687    1.025412
                       1223  |   .1542643   .6493922     0.24   0.812    -1.121616    1.430144
                       1231  |   .8412065   .9255335     0.91   0.364    -.9772164    2.659629
                       1232  |   .5712811   .7742615     0.74   0.461    -.9499333    2.092495
                       1233  |   .4382815   .6416852     0.68   0.495    -.8224563    1.699019
                       1311  |   1.065212   .7737563     1.38   0.169    -.4550098    2.585434
                       1312  |   .1833136   .6990863     0.26   0.793    -1.190202    1.556829
                       1313  |  -.0197352   .6598488    -0.03   0.976     -1.31616    1.276689
                       1321  |   .2012404   .5995631     0.34   0.737    -.9767388     1.37922
                       1322  |  -.0563584    .607726    -0.09   0.926    -1.250375    1.137659
                       1323  |   .2612762   .6079367     0.43   0.668    -.9331549    1.455707
                       1331  |   .2395848   .6670633     0.36   0.720    -1.071014    1.550184
                       1332  |   .0322262   .6858586     0.05   0.963      -1.3153    1.379753
                       1333  |   .2362152   .6756155     0.35   0.727    -1.091186    1.563617
                       2010  |   2.183993   .9251117     2.36   0.019     .3663989    4.001587
                       2020  |   -.433056   .9251117    -0.47   0.640     -2.25065    1.384538
                       2030  |   .0345619   .7398381     0.05   0.963     -1.41902    1.488144
                       3115  |   .1012329   .6155303     0.16   0.869    -1.108118    1.310583
                       3116  |    .399545   .6117033     0.65   0.514    -.8022866    1.601376
                       3117  |   .1572389   .5975647     0.26   0.793    -1.016814    1.331292
                       3120  |   -.239685   .8271681    -0.29   0.772    -1.864846    1.385476
                       3135  |  -.3703676   .7163669    -0.52   0.605    -1.777835    1.037099
                       3136  |  -.1383321    .773944    -0.18   0.858    -1.658923    1.382258
                       3137  |  -.2869523   .6859601    -0.42   0.676    -1.634678    1.060774
                       3215  |  -.2264785   .6218841    -0.36   0.716    -1.448312    .9953555
                       3216  |   .0134455   .6275702     0.02   0.983     -1.21956    1.246451
                       3217  |  -.0439882   .6239501    -0.07   0.944    -1.269881    1.181905
                       3220  |  -.2076646   .6995627    -0.30   0.767    -1.582116    1.166787
                       3235  |  -.0728057   .6413472    -0.11   0.910    -1.332879    1.187268
                       3236  |  -.1984465   .7762596    -0.26   0.798    -1.723587    1.326694
                       3237  |  -.3501213   .8272071    -0.42   0.672    -1.975359    1.275117
                       3315  |  -.0053664   .7001877    -0.01   0.994    -1.381046    1.370313
                       3316  |   .4648941   .7407582     0.63   0.531    -.9904953    1.920283
                       3317  |  -.5946759   .7398408    -0.80   0.422    -2.048263     .858911
                       3320  |  -.2941718   .8274753    -0.36   0.722    -1.919937    1.331593
                       3330  |   .0458553   .7172237     0.06   0.949    -1.363295    1.455006
                             |
                       _cons |  -.5094913   .5914836    -0.86   0.389    -1.671597     .652614
----------------------------------------------------------------------------------------------

. lincom c.abs_votes_abortion_inw1LPP3N 

 ( 1)  abs_votes_abortion_inw1LPP3N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7820316   .1278193     6.12   0.000     .5309013    1.033162
------------------------------------------------------------------------------

. matrix EV2[4,5] = r(estimate)

. matrix EV2[4,6] = r(se)

. 
. 
. regress punish_FaST c.diffYHN  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       534
-------------+----------------------------------   F(41, 492)      =      1.30
       Model |  53.9180062        41  1.31507332   Prob > F        =    0.1040
    Residual |  496.427571       492  1.00899913   R-squared       =    0.0980
-------------+----------------------------------   Adj R-squared   =    0.0228
       Total |  550.345577       533   1.0325433   Root MSE        =    1.0045

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     diffYHN |   .7012136   .2552069     2.75   0.006     .1997838    1.202643
             |
       block |
       1120  |  -1.401794   .7103887    -1.97   0.049    -2.797564    -.006024
       1130  |  -1.485137   .9211766    -1.61   0.108    -3.295062    .3247887
       1210  |  -1.060665   .6393203    -1.66   0.098    -2.316799    .1954701
       1221  |  -1.316021     .73405    -1.79   0.074     -2.75828    .1262387
       1222  |  -1.048183    .640668    -1.64   0.102    -2.306966    .2105999
       1223  |  -.7463335   .6355552    -1.17   0.241    -1.995071    .5024037
       1231  |  -.4995032   .9195355    -0.54   0.587    -2.306204    1.307198
       1232  |  -1.322774   .7677338    -1.72   0.086    -2.831215    .1856675
       1233  |   -1.04836   .6353807    -1.65   0.100    -2.296754    .2000346
       1311  |  -1.228635   .7672106    -1.60   0.110    -2.736048    .2787787
       1312  |  -1.200842   .6933375    -1.73   0.084    -2.563109    .1614258
       1313  |  -1.264091    .654306    -1.93   0.054     -2.54967    .0214877
       1321  |  -.8147594   .5953136    -1.37   0.172     -1.98443    .3549112
       1322  |  -.7474977   .6024023    -1.24   0.215    -1.931096    .4361007
       1323  |  -.9408393   .6029953    -1.56   0.119    -2.125603    .2439242
       1331  |  -1.211685   .6612389    -1.83   0.067    -2.510886    .0875153
       1332  |  -1.267986   .6697189    -1.89   0.059    -2.583848    .0478762
       1333  |  -1.134518    .669683    -1.69   0.091    -2.450309    .1812734
       2010  |  -1.277523   .9170528    -1.39   0.164    -3.079346    .5243004
       2020  |   .4708202   .9186131     0.51   0.609    -1.334069    2.275709
       2030  |  -.3297342   .7336759    -0.45   0.653    -1.771259     1.11179
       3115  |  -.4178546   .6113767    -0.68   0.495    -1.619086    .7833767
       3116  |  -.8714441   .6073746    -1.43   0.152    -2.064812    .3219238
       3117  |  -.9588375   .5924245    -1.62   0.106    -2.122832    .2051566
       3120  |  -1.241234   .8201626    -1.51   0.131    -2.852688    .3702191
       3135  |  -.5868938   .7103946    -0.83   0.409    -1.982675    .8088876
       3136  |  -.1281216   .8214867    -0.16   0.876    -1.742177    1.485933
       3137  |  -1.415103     .68064    -2.08   0.038    -2.752422   -.0777829
       3215  |   -.895753   .6166057    -1.45   0.147    -2.107258    .3157523
       3216  |   -1.18455    .624091    -1.90   0.058    -2.410762    .0416624
       3217  |   -1.09876   .6199851    -1.77   0.077    -2.316905    .1193849
       3220  |  -1.405188   .6956569    -2.02   0.044    -2.772013   -.0383635
       3235  |  -1.090524   .6353804    -1.72   0.087    -2.338918    .1578692
       3236  |  -.8940561   .7672396    -1.17   0.244    -2.401526    .6134142
       3237  |  -1.297674   .8203991    -1.58   0.114    -2.909592    .3142444
       3315  |  -.6104332   .6941211    -0.88   0.380    -1.974241    .7533741
       3316  |  -.1398225    .733597    -0.19   0.849    -1.581192    1.301547
       3317  |  -.9211134    .767413    -1.20   0.231    -2.428925    .5866977
       3320  |  -.9567341   .7680585    -1.25   0.213    -2.465814    .5523453
       3330  |    .359881   .7336961     0.49   0.624    -1.081683    1.801445
             |
       _cons |   .5065618   .5958169     0.85   0.396    -.6640976    1.677221
------------------------------------------------------------------------------

. lincom c.diffYHN 

 ( 1)  diffYHN = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7012136   .2552069     2.75   0.006     .1997838    1.202643
------------------------------------------------------------------------------

. matrix EV2[5,1] = r(estimate)

. matrix EV2[5,2] = r(se)

. regress punish_FaST c.diffYH2N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       534
-------------+----------------------------------   F(41, 492)      =      1.35
       Model |  55.7543741        41  1.35986278   Prob > F        =    0.0758
    Residual |  494.591203       492  1.00526667   R-squared       =    0.1013
-------------+----------------------------------   Adj R-squared   =    0.0264
       Total |  550.345577       533   1.0325433   Root MSE        =    1.0026

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    diffYH2N |   .7841936   .2557181     3.07   0.002     .2817593    1.286628
             |
       block |
       1120  |  -1.414652   .7091306    -1.99   0.047     -2.80795    -.021354
       1130  |  -1.536169   .9202337    -1.67   0.096    -3.344242    .2719039
       1210  |  -1.055675   .6381306    -1.65   0.099    -2.309472    .1981222
       1221  |  -1.306625   .7325066    -1.78   0.075    -2.745852    .1326015
       1222  |   -1.07597   .6398106    -1.68   0.093    -2.333069    .1811279
       1223  |  -.7587142   .6344447    -1.20   0.232    -2.005269     .487841
       1231  |  -.5102494   .9175778    -0.56   0.578    -2.313104    1.292605
       1232  |  -1.321919    .766198    -1.73   0.085    -2.827343    .1835046
       1233  |  -1.056535   .6342327    -1.67   0.096    -2.302673    .1896042
       1311  |  -1.238721   .7657728    -1.62   0.106     -2.74331     .265867
       1312  |  -1.209037   .6919716    -1.75   0.081    -2.568621    .1505475
       1313  |  -1.271826   .6531172    -1.95   0.052    -2.555069    .0114171
       1321  |  -.8205658   .5942264    -1.38   0.168      -1.9881    .3469688
       1322  |  -.7512402   .6012909    -1.25   0.212    -1.932655    .4301747
       1323  |  -.9453672   .6018806    -1.57   0.117    -2.127941    .2372062
       1331  |  -1.218754   .6600245    -1.85   0.065    -2.515568    .0780605
       1332  |  -1.286256   .6685653    -1.92   0.055    -2.599852    .0273389
       1333  |  -1.141303   .6684589    -1.71   0.088    -2.454689    .1720834
       2010  |  -1.290011   .9153973    -1.41   0.159    -3.088581    .5085594
       2020  |   .4466478   .9170511     0.49   0.626    -1.355172    2.248467
       2030  |  -.3360344   .7322705    -0.46   0.647    -1.774798    1.102729
       3115  |  -.4153902   .6102434    -0.68   0.496    -1.614395    .7836144
       3116  |  -.8757835   .6062581    -1.44   0.149    -2.066958    .3153908
       3117  |  -.9587708   .5913261    -1.62   0.106    -2.120607    .2030652
       3120  |   -1.24499    .818644    -1.52   0.129     -2.85346    .3634795
       3135  |  -.5867777   .7090581    -0.83   0.408    -1.979933    .8063779
       3136  |  -.1389679   .8198971    -0.17   0.865      -1.7499    1.471964
       3137  |  -1.425111   .6793947    -2.10   0.036    -2.759984    -.090238
       3215  |  -.8973624   .6154642    -1.46   0.145    -2.106625    .3119002
       3216  |  -1.181692    .622938    -1.90   0.058    -2.405639     .042255
       3217  |  -1.109984   .6188429    -1.79   0.073    -2.325885    .1059173
       3220  |  -1.426919   .6944606    -2.05   0.040    -2.791393   -.0624445
       3235  |  -1.087399   .6342034    -1.71   0.087     -2.33348    .1586825
       3236  |  -.8953776   .7658143    -1.17   0.243    -2.400048    .6092924
       3237  |  -1.298439   .8188398    -1.59   0.113    -2.907293    .3104155
       3315  |  -.6039212   .6927539    -0.87   0.384    -1.965042    .7571998
       3316  |  -.1474119    .732256    -0.20   0.841    -1.586146    1.291323
       3317  |  -.9381248   .7660891    -1.22   0.221    -2.443335     .567085
       3320  |  -.9422078   .7666852    -1.23   0.220    -2.448589    .5641733
       3330  |   .3567046   .7322983     0.49   0.626    -1.082113    1.795522
             |
       _cons |   .4709164   .5941827     0.79   0.428    -.6965323    1.638365
------------------------------------------------------------------------------

. lincom c.diffYH2N

 ( 1)  diffYH2N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7841936   .2557181     3.07   0.002     .2817593    1.286628
------------------------------------------------------------------------------

. matrix EV2[5,3] = r(estimate)

. matrix EV2[5,4] = r(se)

. regress punish_FaST c.diffLPP3N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       534
-------------+----------------------------------   F(41, 492)      =      1.26
       Model |  52.1814092        41   1.2727173   Prob > F        =    0.1377
    Residual |  498.164168       492   1.0125288   R-squared       =    0.0948
-------------+----------------------------------   Adj R-squared   =    0.0194
       Total |  550.345577       533   1.0325433   Root MSE        =    1.0062

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   diffLPP3N |   .6638673   .2754652     2.41   0.016     .1226341      1.2051
             |
       block |
       1120  |  -1.426866   .7119438    -2.00   0.046    -2.825691   -.0280407
       1130  |  -1.479547   .9237786    -1.60   0.110    -3.294584    .3354909
       1210  |  -1.094203   .6402168    -1.71   0.088    -2.352099    .1636937
       1221  |  -1.360051   .7364469    -1.85   0.065     -2.80702    .0869182
       1222  |  -1.079176   .6434225    -1.68   0.094    -2.343371     .185019
       1223  |  -.7863537   .6374997    -1.23   0.218    -2.038911     .466204
       1231  |  -.5137246     .92242    -0.56   0.578    -2.326093    1.298644
       1232  |  -1.355072   .7699252    -1.76   0.079    -2.867819    .1576754
       1233  |   -1.08311   .6369493    -1.70   0.090    -2.334587    .1683659
       1311  |  -1.266556   .7685918    -1.65   0.100    -2.776683     .243571
       1312  |  -1.240871   .6943758    -1.79   0.075    -2.605178    .1234371
       1313  |  -1.298492   .6558035    -1.98   0.048    -2.587013    -.009971
       1321  |  -.8627569   .5969601    -1.45   0.149    -2.035663    .3101488
       1322  |     -.7966   .6038649    -1.32   0.188    -1.983072    .3898722
       1323  |  -.9910701   .6044058    -1.64   0.102    -2.178605    .1964648
       1331  |  -1.248614   .6626252    -1.88   0.060    -2.550538    .0533107
       1332  |  -1.298827   .6712226    -1.94   0.054    -2.617644     .019989
       1333  |  -1.169559   .6711559    -1.74   0.082    -2.488245     .149126
       2010  |  -1.302516    .918898    -1.42   0.157    -3.107964    .5029329
       2020  |   .4355009   .9218171     0.47   0.637    -1.375683    2.246685
       2030  |  -.3468756   .7348882    -0.47   0.637    -1.790782    1.097031
       3115  |  -.4339392   .6123906    -0.71   0.479    -1.637163    .7692842
       3116  |  -.8906612   .6085645    -1.46   0.144    -2.086367    .3050448
       3117  |  -.9714595   .5934528    -1.64   0.102    -2.137474    .1945551
       3120  |  -1.291451   .8218364    -1.57   0.117    -2.906193    .3232908
       3135  |  -.5977796   .7115921    -0.84   0.401    -1.995914    .8003548
       3136  |  -.1438379   .8237605    -0.17   0.861     -1.76236    1.474685
       3137  |   -1.41929   .6820911    -2.08   0.038    -2.759461   -.0791197
       3215  |  -.9106765   .6177116    -1.47   0.141    -2.124355    .3030017
       3216  |   -1.19686   .6251479    -1.91   0.056    -2.425149    .0314288
       3217  |  -1.112982   .6210865    -1.79   0.074    -2.333291    .1073275
       3220  |   -1.41316   .6979343    -2.02   0.043    -2.784459   -.0418606
       3235  |  -1.122843    .636407    -1.76   0.078    -2.373254    .1275681
       3236  |  -.9239318   .7688525    -1.20   0.230    -2.434571    .5867077
       3237  |  -1.335709   .8224858    -1.62   0.105    -2.951727    .2803091
       3315  |  -.6541983   .6947689    -0.94   0.347    -2.019278    .7108817
       3316  |  -.1479547   .7349226    -0.20   0.841    -1.591929    1.296019
       3317  |  -.9266979   .7688865    -1.21   0.229    -2.437404    .5840083
       3320  |  -1.024708   .7686489    -1.33   0.183    -2.534947    .4855313
       3330  |   .3196232    .734859     0.43   0.664    -1.124226    1.763472
             |
       _cons |    .553694   .5967082     0.93   0.354    -.6187166    1.726105
------------------------------------------------------------------------------

. lincom c.diffLPP3N

 ( 1)  diffLPP3N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6638673   .2754652     2.41   0.016     .1226341      1.2051
------------------------------------------------------------------------------

. matrix EV2[5,5] = r(estimate)

. matrix EV2[5,6] = r(se)

. 
. 
. regress proportionST c.diffYHN  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       528
-------------+----------------------------------   F(41, 486)      =      1.46
       Model |  48.9156499        41  1.19306463   Prob > F        =    0.0354
    Residual |  396.365958       486  .815567814   R-squared       =    0.1099
-------------+----------------------------------   Adj R-squared   =    0.0348
       Total |  445.281607       527  .844936637   Root MSE        =    .90309

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     diffYHN |   .8626998   .2306435     3.74   0.000     .4095183    1.315881
             |
       block |
       1120  |  -1.038994   .6386772    -1.63   0.104    -2.293904    .2159152
       1130  |  -1.183714   .8282249    -1.43   0.154    -2.811058    .4436299
       1210  |  -.6639455   .5747843    -1.16   0.249    -1.793315    .4654237
       1221  |  -.9756514   .6599533    -1.48   0.140    -2.272365    .3210627
       1222  |   -.659628   .5760087    -1.15   0.253    -1.791403    .4721468
       1223  |  -.4456007   .5713994    -0.78   0.436    -1.568319    .6771175
       1231  |    .211151   .8267341     0.26   0.799    -1.413263    1.835565
       1232  |  -.9839596   .6902374    -1.43   0.155    -2.340178    .3722584
       1233  |  -.6203392   .5712409    -1.09   0.278    -1.742746    .5020675
       1311  |  -.8681405   .6897621    -1.26   0.209    -2.223425    .4871435
       1312  |  -.8339472   .6233479    -1.34   0.182    -2.058737    .3908423
       1313  |  -.9117625   .5882553    -1.55   0.122      -2.0676    .2440752
       1321  |  -.4634845   .5354735    -0.87   0.387    -1.515613    .5886444
       1322  |  -.4600401   .5415908    -0.85   0.396    -1.524189    .6041085
       1323  |  -.3746531   .5426886    -0.69   0.490    -1.440959    .6916524
       1331  |  -.7602188    .594488    -1.28   0.202    -1.928303    .4078652
       1332  |  -.9165541   .6021125    -1.52   0.129    -2.099619    .2665109
       1333  |  -.3143801   .6020798    -0.52   0.602    -1.497381    .8686208
       2010  |  -.9282872   .8244786    -1.13   0.261     -2.54827    .6916956
       2020  |   .2575264   .8258961     0.31   0.755    -1.365242    1.880294
       2030  |   .4310857   .6596135     0.65   0.514    -.8649606    1.727132
       3115  |   .0003244   .5496598     0.00   1.000    -1.079679    1.080327
       3116  |  -.4302936    .546872    -0.79   0.432    -1.504819    .6442317
       3117  |  -.5073809   .5326203    -0.95   0.341    -1.553904     .539142
       3120  |  -.8836419   .7373686    -1.20   0.231    -2.332466    .5651821
       3135  |  -.4003313   .6386825    -0.63   0.531    -1.655251    .8545887
       3136  |  -.1576274   .7385715    -0.21   0.831    -1.608815     1.29356
       3137  |  -.7004987   .6389376    -1.10   0.273     -1.95592    .5549225
       3215  |  -.5850161   .5543604    -1.06   0.292    -1.674255    .5042229
       3216  |  -.7222519   .5610904    -1.29   0.199    -1.824714    .3802107
       3217  |  -.4711411   .5591377    -0.84   0.400    -1.569767    .6274847
       3220  |  -1.085354   .6254549    -1.74   0.083    -2.314283    .1435759
       3235  |  -.6296032   .5712406    -1.10   0.271    -1.752009     .492803
       3236  |   -.250647   .6897885    -0.36   0.716    -1.605983    1.104689
       3237  |   -.953079   .7375834    -1.29   0.197    -2.402325    .4961672
       3315  |  -.3833315   .6240597    -0.61   0.539     -1.60952    .8428568
       3316  |    .158252   .6595418     0.24   0.810    -1.137653    1.454157
       3317  |  -.2839355    .689946    -0.41   0.681    -1.639581     1.07171
       3320  |  -.0982777   .6905325    -0.14   0.887    -1.455075     1.25852
       3330  |   .4882092   .6596319     0.74   0.460    -.8078731    1.784292
             |
       _cons |  -.0284914   .5358177    -0.05   0.958    -1.081297    1.024314
------------------------------------------------------------------------------

. lincom c.diffYHN 

 ( 1)  diffYHN = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .8626998   .2306435     3.74   0.000     .4095183    1.315881
------------------------------------------------------------------------------

. matrix EV2[6,1] = r(estimate)

. matrix EV2[6,2] = r(se)

. regress proportionST c.diffYH2N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       528
-------------+----------------------------------   F(41, 486)      =      1.53
       Model |  50.9949268        41   1.2437787   Prob > F        =    0.0209
    Residual |  394.286681       486  .811289467   R-squared       =    0.1145
-------------+----------------------------------   Adj R-squared   =    0.0398
       Total |  445.281607       527  .844936637   Root MSE        =    .90072

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    diffYH2N |   .9404074   .2306242     4.08   0.000     .4872639    1.393551
             |
       block |
       1120  |  -1.053358   .6370511    -1.65   0.099    -2.305073    .1983561
       1130  |  -1.237399     .82673    -1.50   0.135    -2.861805    .3870075
       1210  |  -.6595044   .5732686    -1.15   0.251    -1.785895    .4668865
       1221  |  -.9621297   .6580519    -1.46   0.144    -2.255108    .3308483
       1222  |  -.6890818   .5747897    -1.20   0.231    -1.818461    .4402979
       1223  |  -.4588941   .5699579    -0.81   0.421     -1.57878    .6609918
       1231  |   .2041289   .8243255     0.25   0.805    -1.415553    1.823811
       1232  |    -.98047   .6883197    -1.42   0.155     -2.33292    .3719798
       1233  |  -.6292506    .569766    -1.10   0.270    -1.748759    .4902582
       1311  |   -.880699   .6879347    -1.28   0.201    -2.232392    .4709945
       1312  |  -.8451009   .6216358    -1.36   0.175    -2.066526    .3763247
       1313  |  -.9203979   .5867306    -1.57   0.117     -2.07324    .2324439
       1321  |  -.4694624   .5340796    -0.88   0.380    -1.518853    .5799278
       1322  |   -.464383   .5401719    -0.86   0.390    -1.525744    .5969776
       1323  |  -.3810547    .541264    -0.70   0.482    -1.444561    .6824519
       1331  |   -.768525   .5929354    -1.30   0.196    -1.933558    .3965084
       1332  |  -.9377031    .600609    -1.56   0.119    -2.117814    .2424078
       1333  |  -.3220389   .6005127    -0.54   0.592    -1.501961    .8578828
       2010  |  -.9422054   .8223513    -1.15   0.252    -2.558008    .6735974
       2020  |   .2332316   .8238486     0.28   0.777    -1.385513    1.851976
       2030  |    .422493   .6578382     0.64   0.521    -.8700651    1.715051
       3115  |   .0025247   .5482148     0.00   0.996    -1.074639    1.079688
       3116  |  -.4353699   .5454442    -0.80   0.425     -1.50709    .6363501
       3117  |  -.5075786     .53122    -0.96   0.340     -1.55135    .5361927
       3120  |  -.8882164   .7354317    -1.21   0.228    -2.333235    .5568019
       3135  |  -.4012758   .6369855    -0.63   0.529    -1.652861    .8503097
       3136  |  -.1666507   .7365663    -0.23   0.821    -1.613898    1.280597
       3137  |  -.7143686   .6373183    -1.12   0.263    -1.966608    .5378708
       3215  |  -.5869664   .5529045    -1.06   0.289    -1.673345    .4994119
       3216  |  -.7194364   .5596188    -1.29   0.199    -1.819007    .3801346
       3217  |  -.4859569    .557676    -0.87   0.384    -1.581711    .6097968
       3220  |  -1.106386   .6238893    -1.77   0.077    -2.332239    .1194673
       3235  |  -.6267448   .5697394    -1.10   0.272    -1.746201    .4927117
       3236  |  -.2514978   .6879723    -0.37   0.715    -1.603265     1.10027
       3237  |  -.9523123    .735609    -1.29   0.196    -2.397679    .4930544
       3315  |  -.3786358   .6223441    -0.61   0.543    -1.601453    .8441813
       3316  |   .1496318    .657825     0.23   0.820      -1.1429    1.442164
       3317  |  -.3027605   .6882211    -0.44   0.660    -1.655017    1.049496
       3320  |  -.0839746   .6887608    -0.12   0.903    -1.437291    1.269342
       3330  |   .4832632   .6578633     0.73   0.463    -.8093443    1.775871
             |
       _cons |  -.0595663   .5338926    -0.11   0.911    -1.108589    .9894565
------------------------------------------------------------------------------

. lincom c.diffYH2N

 ( 1)  diffYH2N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9404074   .2306242     4.08   0.000     .4872639    1.393551
------------------------------------------------------------------------------

. matrix EV2[6,3] = r(estimate)

. matrix EV2[6,4] = r(se)

. regress proportionST c.diffLPP3N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       528
-------------+----------------------------------   F(41, 486)      =      1.41
       Model |  47.2985555        41   1.1536233   Prob > F        =    0.0520
    Residual |  397.983052       486  .818895169   R-squared       =    0.1062
-------------+----------------------------------   Adj R-squared   =    0.0308
       Total |  445.281607       527  .844936637   Root MSE        =    .90493

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   diffLPP3N |   .8630933   .2495798     3.46   0.001     .3727046    1.353482
             |
       block |
       1120  |  -1.073959   .6402654    -1.68   0.094    -2.331989    .1840706
       1130  |  -1.193313   .8308353    -1.44   0.152    -2.825786    .4391596
       1210  |  -.7040853   .5757552    -1.22   0.222    -1.835362    .4271914
       1221  |  -1.037956    .662317    -1.57   0.118    -2.339315     .263402
       1222  |   -.708608   .5786813    -1.22   0.221    -1.845634     .428418
       1223  |  -.5011192    .573326    -0.87   0.383    -1.627623    .6253845
       1231  |   .1794949   .8295953     0.22   0.829    -1.450541    1.809531
       1232  |  -1.031483   .6924219    -1.49   0.137    -2.391993    .3290269
       1233  |  -.6675208   .5728236    -1.17   0.244    -1.793037    .4579956
       1311  |  -.9164041   .6912047    -1.33   0.186    -2.274523    .4417144
       1312  |  -.8830106   .6244606    -1.41   0.158    -2.109987    .3439653
       1313  |  -.9579241   .5897774    -1.62   0.105    -2.116753    .2009043
       1321  |  -.5270115    .537141    -0.98   0.327    -1.582417    .5283939
       1322  |  -.5242037   .5430686    -0.97   0.335    -1.591256    .5428486
       1323  |  -.4390696   .5440929    -0.81   0.420    -1.508134    .6299952
       1331  |  -.8086123   .5959101    -1.36   0.175    -1.979491    .3622659
       1332  |    -.95836   .6036439    -1.59   0.113    -2.144434    .2277141
       1333  |  -.3610097    .603583    -0.60   0.550    -1.546964    .8249448
       2010  |   -.963155   .8263805    -1.17   0.244    -2.586875    .6605648
       2020  |   .2010722    .829045     0.24   0.808    -1.427883    1.830027
       2030  |   .4111295   .6608943     0.62   0.534    -.8874335    1.709692
       3115  |  -.0188924   .5507304    -0.03   0.973    -1.100999    1.063214
       3116  |  -.4553083    .548113    -0.83   0.407    -1.532272    .6216554
       3117  |  -.5231671   .5336994    -0.98   0.327     -1.57181     .525476
       3120  |  -.9487701   .7390907    -1.28   0.200    -2.400978    .5034375
       3135  |  -.4120505   .6399444    -0.64   0.520     -1.66945    .8453488
       3136  |  -.1870035    .740847    -0.25   0.801    -1.642662    1.268655
       3137  |  -.7092117   .6403949    -1.11   0.269    -1.967496    .5490727
       3215  |  -.6043727    .555516    -1.09   0.277    -1.695882    .4871368
       3216  |  -.7368821   .5622032    -1.31   0.191    -1.841531    .3677669
       3217  |  -.4898267   .5602947    -0.87   0.382    -1.590726    .6110723
       3220  |  -1.107004   .6277087    -1.76   0.078    -2.340362     .126354
       3235  |  -.6696217   .5723286    -1.17   0.243    -1.794166    .4549222
       3236  |  -.2911359   .6914427    -0.42   0.674    -1.649722     1.06745
       3237  |   -1.00631   .7396835    -1.36   0.174    -2.459682    .4470628
       3315  |  -.4332402   .6248194    -0.69   0.488    -1.660921    .7944407
       3316  |   .1465994   .6609257     0.22   0.825    -1.152025    1.445224
       3317  |  -.2947321   .6914738    -0.43   0.670    -1.653379    1.063915
       3320  |   -.179653   .6912569    -0.26   0.795    -1.537874    1.178568
       3330  |   .4384228   .6608676     0.66   0.507    -.8600876    1.736933
             |
       _cons |   .0065823   .5368365     0.01   0.990    -1.048225    1.061389
------------------------------------------------------------------------------

. lincom c.diffLPP3N

 ( 1)  diffLPP3N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .8630933   .2495798     3.46   0.001     .3727046    1.353482
------------------------------------------------------------------------------

. matrix EV2[6,5] = r(estimate)

. matrix EV2[6,6] = r(se)

. 
. 
. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. 
. putexcel set  "$pathtemp/EV2", replace
note: file will be replaced when the first putexcel command is issued.

. putexcel A1=matrix(EV2) 
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp/EV2.xlsx saved

. 
. 
. 
. **** code for the figure 
. 
. 
. preserve

. 
. clear all

. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. import excel EV2
(6 vars, 6 obs)

. 
. gen topic = _n

. 
. 
. rename A est1

. rename B se1

. rename C est2

. rename D se2

. rename E est3

. rename F se3

. 
. reshape long est se, i(topic) j(method)    
(j = 1 2 3)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations                6   ->   18          
Number of variables                   7   ->   4           
j variable (3 values)                     ->   method
xij variables:
                         est1 est2 est3   ->   est
                            se1 se2 se3   ->   se
-----------------------------------------------------------------------------

. 
. sort topic method

. gen order = _n

. gen lb = est - 1.96*se

. gen ub = est + 1.96*se

. 
. 
. tw (scatter est order,  legend(off) ylabel(0(0.2)2.2) yline(0, lc(black) lstyle(gs15)) ///
> text(2.2 1.5 "Gun", place(e)) ///
> text(2.2 3.9 "Immigration", place(e)) ///
> text(2.2 7.2 "Minimum", place(e)) ///
> text(2.0 7.4 "Wage", place(e)) ///
> text(2.2 10 "Abortion", place(e)) ///
> text(2.2 12.5 "DG punish (1)", place(e)) ///
> text(2.2 15.8 "DG punish (2)", place(e)) ///
> xlabel(1 "YHL" 2 "YHQ" 3 "Multi" 4 " " 5 " " 6 " " 7 "YHL" 8 "YHQ" 9 "Multi" ///
> 10 " " 11 " " 12 " " 13 "YHL" 14 "YHQ" 15 "Multi" 16 " " 17 " " 18 " " , angle(45)labsize(small) ) ///
> xtitle(" ", size(zero))) ///
> (rcap lb ub order, xline(3.5) xline(6.5) xline(9.5) xline(12.5) xline(15.5))
(note:  named style gs15 not found in class linestyle, default attributes used)

. 
. cd "$pathfig"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig

. graph export "FigE2_bot.pdf", replace 
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/FigE2_bot.pdf saved as PDF format

. 
. 
. restore 

. 
. 
. **************************************************
. * OUTPUT FIG E2                                  *
. * SEE "FigE2_opt.pdf" & "FigE2_bot.pdf" IN "FIG" *
. **************************************************
. 
. 
. 
. 
. *----- 
. *-------------------------------
. *---  E. Alternative Approaches to Likert+ -- Figure E3
. 
. 
. 
. 
. matrix EV3 = J(6,6,0)

. matrix colnames EV3 = "Likert" " " "Likert + (YHQ)" " " "QVSR" " " 

. matrix rownames EV3 = "Gun-related Donations" ///
> "Immigration-related Donations" ///
> "Minimum Wage-related Writing" /// 
> "Abortion-related Writing" ///
> "Punish abs" ///
> "Punish proportion"

. 
. 
. regress don_C_gunST c.votes_gunw1LPP3N##i.method i.block

      Source |       SS           df       MS      Number of obs   =     3,670
-------------+----------------------------------   F(45, 3624)     =     22.38
       Model |  798.525398        45  17.7450088   Prob > F        =    0.0000
    Residual |  2872.94166     3,624   .79275432   R-squared       =    0.2175
-------------+----------------------------------   Adj R-squared   =    0.2078
       Total |  3671.46705     3,669  1.00067241   Root MSE        =    .89037

-------------------------------------------------------------------------------------------
              don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
         votes_gunw1LPP3N |   .9155763   .0727683    12.58   0.000     .7729054    1.058247
                          |
                   method |
                 Likert+  |  -.0586373   .0777224    -0.75   0.451    -.2110214    .0937467
                    QVSR  |  -.2396741   .0938318    -2.55   0.011    -.4236425   -.0557056
                          |
method#c.votes_gunw1LPP3N |
                 Likert+  |    .185417    .102993     1.80   0.072    -.0165131    .3873471
                    QVSR  |   .5842363   .1402445     4.17   0.000     .3092703    .8592024
                          |
                    block |
                    1120  |  -.2487885    .250691    -0.99   0.321     -.740298    .2427209
                    1130  |  -.3966028   .2549053    -1.56   0.120    -.8963751    .1031694
                    1210  |  -.2927699   .2067436    -1.42   0.157    -.6981152    .1125754
                    1221  |   -.413507   .2225652    -1.86   0.063    -.8498726    .0228586
                    1222  |   -.266392   .2122754    -1.25   0.210    -.6825832    .1497992
                    1223  |  -.2843109   .2052026    -1.39   0.166    -.6866349    .1180132
                    1231  |  -.5636415   .3174459    -1.78   0.076    -1.186032     .058749
                    1232  |   -.177901   .2377542    -0.75   0.454    -.6440462    .2882443
                    1233  |  -.3604687    .210161    -1.72   0.086    -.7725144     .051577
                    1311  |  -.4085868    .246968    -1.65   0.098    -.8927969    .0756234
                    1312  |  -.3226036   .2250986    -1.43   0.152    -.7639362    .1187291
                    1313  |  -.4797113   .2152738    -2.23   0.026     -.901781   -.0576415
                    1321  |  -.4226038    .190761    -2.22   0.027    -.7966135   -.0485942
                    1322  |  -.3706053   .1964165    -1.89   0.059    -.7557032    .0144925
                    1323  |   -.468324   .1960948    -2.39   0.017     -.852791   -.0838569
                    1331  |    -.42291   .2275173    -1.86   0.063    -.8689847    .0231647
                    1332  |  -.3655984   .2266153    -1.61   0.107    -.8099046    .0787077
                    1333  |  -.3856322   .2165425    -1.78   0.075    -.8101894     .038925
                    2010  |  -.3518421   .2722425    -1.29   0.196    -.8856059    .1819216
                    2020  |  -.2324373   .3846156    -0.60   0.546    -.9865219    .5216473
                    2030  |  -.1904329   .2352666    -0.81   0.418    -.6517011    .2708353
                    3115  |   .0548008   .1977886     0.28   0.782    -.3329873    .4425889
                    3116  |  -.0562471   .1987009    -0.28   0.777    -.4458238    .3333296
                    3117  |   .0325985   .1898481     0.17   0.864    -.3396212    .4048182
                    3120  |  -.3004722    .257411    -1.17   0.243    -.8051571    .2042127
                    3135  |  -.4259117   .2266637    -1.88   0.060    -.8703129    .0184895
                    3136  |  -.2722979   .2434767    -1.12   0.263    -.7496629    .2050672
                    3137  |  -.0909896   .2251801    -0.40   0.686     -.532482    .3505028
                    3215  |  -.0713798   .1988715    -0.36   0.720    -.4612911    .3185315
                    3216  |  -.0428566   .2017429    -0.21   0.832    -.4383974    .3526843
                    3217  |  -.0788716   .1993408    -0.40   0.692     -.469703    .3119597
                    3220  |  -.2084613   .2243673    -0.93   0.353    -.6483601    .2314374
                    3235  |  -.4112227   .2040383    -2.02   0.044     -.811264   -.0111815
                    3236  |  -.3645233   .2258646    -1.61   0.107    -.8073576    .0783111
                    3237  |  -.4279121   .2550183    -1.68   0.093    -.9279057    .0720815
                    3315  |   -.008438   .2341514    -0.04   0.971    -.4675195    .4506436
                    3316  |  -.1545187   .2575824    -0.60   0.549    -.6595395    .3505022
                    3317  |  -.2565085    .239322    -1.07   0.284    -.7257276    .2127107
                    3320  |  -.8715335    .262947    -3.31   0.001    -1.387072   -.3559946
                    3330  |  -.3128962   .2237111    -1.40   0.162    -.7515084    .1257159
                          |
                    _cons |  -.4159692   .1933267    -2.15   0.031    -.7950093   -.0369292
-------------------------------------------------------------------------------------------

. lincom c.votes_gunw1LPP3N + 1.method#c.votes_gunw1LPP3N 

 ( 1)  votes_gunw1LPP3N + 1b.method#co.votes_gunw1LPP3N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9155763   .0727683    12.58   0.000     .7729054    1.058247
------------------------------------------------------------------------------

. matrix EV3[1,1] = r(estimate)

. matrix EV3[1,2] = r(se)

. lincom c.votes_gunw1LPP3N + 3.method#c.votes_gunw1LPP3N 

 ( 1)  votes_gunw1LPP3N + 3.method#c.votes_gunw1LPP3N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.499813   .1288103    11.64   0.000     1.247265    1.752361
------------------------------------------------------------------------------

. matrix EV3[1,5] = r(estimate)

. matrix EV3[1,6] = r(se)

. regress don_C_gunST c.YH_gun2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,260
-------------+----------------------------------   F(41, 1218)     =      9.03
       Model |  321.580751        41  7.84343294   Prob > F        =    0.0000
    Residual |  1057.38716     1,218  .868133954   R-squared       =    0.2332
-------------+----------------------------------   Adj R-squared   =    0.2074
       Total |  1378.96791     1,259  1.09528825   Root MSE        =    .93174

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    YH_gun2N |   1.095523   .0994431    11.02   0.000     .9004238    1.290621
             |
       block |
       1120  |  -.8536684   .4594059    -1.86   0.063    -1.754983    .0476464
       1130  |  -.9865374   .4695526    -2.10   0.036    -1.907759   -.0653157
       1210  |   -.763622   .3867229    -1.97   0.049    -1.522339    -.004905
       1221  |   -.799532   .4155293    -1.92   0.055    -1.614764    .0157006
       1222  |  -.6095994   .3937703    -1.55   0.122    -1.382143    .1629438
       1223  |  -.7767116   .3877689    -2.00   0.045    -1.537481   -.0159426
       1231  |  -1.134967   .5849274    -1.94   0.053    -2.282544    .0126106
       1232  |  -.5921974   .4506568    -1.31   0.189    -1.476347    .2919523
       1233  |  -.7318895   .3951893    -1.85   0.064    -1.507217    .0434377
       1311  |  -1.134046   .4695971    -2.41   0.016    -2.055355   -.2127374
       1312  |  -.6176811   .4184351    -1.48   0.140    -1.438615    .2032524
       1313  |  -.9649417   .4023371    -2.40   0.017    -1.754292    -.175591
       1321  |  -.7999848    .361019    -2.22   0.027    -1.508273   -.0916968
       1322  |  -.7026623   .3692049    -1.90   0.057     -1.42701    .0216858
       1323  |  -.9364108    .369801    -2.53   0.011    -1.661928   -.2108932
       1331  |  -.7486082   .4368704    -1.71   0.087     -1.60571    .1084938
       1332  |  -.8888877   .4222757    -2.10   0.035    -1.717356   -.0604192
       1333  |  -.8700219   .4044051    -2.15   0.032     -1.66343   -.0766141
       2010  |   -.574125   .5184719    -1.11   0.268    -1.591322    .4430721
       2020  |  -.3428842   .6430494    -0.53   0.594    -1.604492    .9187231
       2030  |  -.8865955   .4314586    -2.05   0.040     -1.73308   -.0401109
       3115  |  -.1932237   .3729006    -0.52   0.604    -.9248224     .538375
       3116  |  -.3825027   .3739572    -1.02   0.307    -1.116174     .351169
       3117  |  -.3394769   .3591908    -0.95   0.345    -1.044178    .3652243
       3120  |  -1.002319   .4592633    -2.18   0.029    -1.903354   -.1012846
       3135  |  -.9299459   .4184436    -2.22   0.026    -1.750896   -.1089958
       3136  |  -.4229061   .4696934    -0.90   0.368    -1.344404    .4985917
       3137  |  -.7056948   .4189136    -1.68   0.092    -1.527567    .1161775
       3215  |  -.5525533   .3746596    -1.47   0.141    -1.287603    .1824963
       3216  |  -.4743355   .3772556    -1.26   0.209    -1.214478    .2658075
       3217  |  -.6918483   .3751788    -1.84   0.065    -1.427917    .0442201
       3220  |  -.7892591   .4184781    -1.89   0.060    -1.610277    .0317588
       3235  |  -.8223519   .3840351    -2.14   0.032    -1.575796   -.0689083
       3236  |  -.8762598   .4222865    -2.08   0.038    -1.704749   -.0477702
       3237  |  -.9120185   .4695856    -1.94   0.052    -1.833305    .0092678
       3315  |   -.513714    .436807    -1.18   0.240    -1.370692    .3432636
       3316  |   -.296778   .4700271    -0.63   0.528     -1.21893    .6253746
       3317  |  -.7141344    .437899    -1.63   0.103    -1.573254    .1449856
       3320  |  -1.485532   .5197452    -2.86   0.004    -2.505227   -.4658366
       3330  |  -.8289675   .4151512    -2.00   0.046    -1.643458   -.0144768
             |
       _cons |  -.0259703   .3576728    -0.07   0.942    -.7276935    .6757529
------------------------------------------------------------------------------

. lincom c.YH_gun2N

 ( 1)  YH_gun2N = 0

------------------------------------------------------------------------------
 don_C_gunST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.095523   .0994431    11.02   0.000     .9004238    1.290621
------------------------------------------------------------------------------

. matrix EV3[1,3] = r(estimate)

. matrix EV3[1,4] = r(se)

. 
. 
. regress don_C_wall_inST c.votes_wall_inw1LPP3N##i.method  i.block

      Source |       SS           df       MS      Number of obs   =     3,670
-------------+----------------------------------   F(45, 3624)     =     14.13
       Model |  547.473851        45  12.1660856   Prob > F        =    0.0000
    Residual |  3120.01838     3,624  .860932223   R-squared       =    0.1493
-------------+----------------------------------   Adj R-squared   =    0.1387
       Total |  3667.49223     3,669  .999589051   Root MSE        =    .92786

-----------------------------------------------------------------------------------------------
              don_C_wall_inST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------------------+----------------------------------------------------------------
         votes_wall_inw1LPP3N |   .5114665   .0751858     6.80   0.000     .3640559    .6588772
                              |
                       method |
                     Likert+  |   -.091872   .0662627    -1.39   0.166    -.2217878    .0380439
                        QVSR  |  -.1924184   .0802178    -2.40   0.017     -.349695   -.0351418
                              |
method#c.votes_wall_inw1LPP3N |
                     Likert+  |     .17196   .1001846     1.72   0.086    -.0244638    .3683838
                        QVSR  |   .3322835   .1322229     2.51   0.012     .0730447    .5915223
                              |
                        block |
                        1120  |  -.3114163   .2622465    -1.19   0.235    -.8255817    .2027491
                        1130  |   .0335254   .2660153     0.13   0.900    -.4880293    .5550801
                        1210  |  -.0207852   .2154553    -0.10   0.923    -.4432109    .4016404
                        1221  |  -.2399344     .23294    -1.03   0.303    -.6966409    .2167721
                        1222  |  -.1680903   .2223797    -0.76   0.450    -.6040921    .2679114
                        1223  |  -.2236336   .2145687    -1.04   0.297     -.644321    .1970539
                        1231  |  -.0612314   .3308735    -0.19   0.853    -.7099481    .5874854
                        1232  |  -.1174615   .2479451    -0.47   0.636    -.6035872    .3686643
                        1233  |  -.0957169   .2193365    -0.44   0.663    -.5257521    .3343184
                        1311  |  -.1441998   .2574376    -0.56   0.575    -.6489369    .3605373
                        1312  |   .0181217     .23457     0.08   0.938    -.4417807    .4780242
                        1313  |  -.1166864   .2243822    -0.52   0.603    -.5566144    .3232415
                        1321  |  -.3316601   .2005127    -1.65   0.098    -.7247891    .0614689
                        1322  |  -.1527018   .2057939    -0.74   0.458    -.5561852    .2507817
                        1323  |  -.4541214   .2058265    -2.21   0.027    -.8576687   -.0505741
                        1331  |  -.0456267   .2376468    -0.19   0.848    -.5115616    .4203081
                        1332  |  -.1863416   .2373464    -0.79   0.432    -.6516874    .2790042
                        1333  |  -.0790731   .2261409    -0.35   0.727    -.5224492     .364303
                        2010  |   .0336627   .2837531     0.12   0.906     -.522669    .5899943
                        2020  |  -.4021774   .4019605    -1.00   0.317    -1.190269    .3859139
                        2030  |  -.1175347   .2451509    -0.48   0.632    -.5981822    .3631127
                        3115  |   .2763631   .2063881     1.34   0.181    -.1282853    .6810115
                        3116  |   .1140679   .2071863     0.55   0.582    -.2921455    .5202814
                        3117  |   .1797462   .1980294     0.91   0.364     -.208514    .5680064
                        3120  |  -.2499802   .2686172    -0.93   0.352    -.7766361    .2766758
                        3135  |   .1167665   .2363518     0.49   0.621    -.3466294    .5801623
                        3136  |  -.1113393   .2537074    -0.44   0.661    -.6087627    .3860841
                        3137  |  -.0445025   .2347597    -0.19   0.850    -.5047768    .4157719
                        3215  |   .1671912   .2073432     0.81   0.420    -.2393298    .5737121
                        3216  |  -.0071516   .2102116    -0.03   0.973    -.4192964    .4049933
                        3217  |    .018074   .2077545     0.09   0.931    -.3892533    .4254013
                        3220  |  -.1778073   .2348607    -0.76   0.449    -.6382797     .282665
                        3235  |  -.0226617   .2126096    -0.11   0.915    -.4395081    .3941848
                        3236  |  -.1344732   .2353613    -0.57   0.568    -.5959271    .3269807
                        3237  |   .1478767   .2656674     0.56   0.578    -.3729957    .6687492
                        3315  |   .0782911   .2439652     0.32   0.748    -.4000317    .5566138
                        3316  |  -.0787637    .268192    -0.29   0.769     -.604586    .4470586
                        3317  |  -.2044169   .2491745    -0.82   0.412    -.6929531    .2841193
                        3320  |  -.1540349   .2745375    -0.56   0.575    -.6922984    .3842286
                        3330  |  -.1035982   .2331457    -0.44   0.657     -.560708    .3535117
                              |
                        _cons |  -.2114292   .2002464    -1.06   0.291     -.604036    .1811776
-----------------------------------------------------------------------------------------------

. lincom c.votes_wall_inw1LPP3N + 1.method#c.votes_wall_inw1LPP3N 

 ( 1)  votes_wall_inw1LPP3N + 1b.method#co.votes_wall_inw1LPP3N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .5114665   .0751858     6.80   0.000     .3640559    .6588772
------------------------------------------------------------------------------

. matrix EV3[2,1] = r(estimate)

. matrix EV3[2,2] = r(se)

. lincom c.votes_wall_inw1LPP3N + 3.method#c.votes_wall_inw1LPP3N 

 ( 1)  votes_wall_inw1LPP3N + 3.method#c.votes_wall_inw1LPP3N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |     .84375   .1320611     6.39   0.000     .5848284    1.102672
------------------------------------------------------------------------------

. matrix EV3[2,5] = r(estimate)

. matrix EV3[2,6] = r(se)

. regress don_C_wall_inST c.YH_wall_in2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =     1,261
-------------+----------------------------------   F(41, 1219)     =      5.89
       Model |  221.688748        41  5.40704263   Prob > F        =    0.0000
    Residual |  1119.30918     1,219   .91821918   R-squared       =    0.1653
-------------+----------------------------------   Adj R-squared   =    0.1372
       Total |  1340.99793     1,260  1.06428407   Root MSE        =    .95824

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
YH_wall_in2N |   .6424546   .1238019     5.19   0.000     .3995661     .885343
             |
       block |
       1120  |  -.2709125   .4751751    -0.57   0.569    -1.203164    .6613391
       1130  |   -.474877   .4842356    -0.98   0.327    -1.424905    .4751506
       1210  |  -.3429695   .3977265    -0.86   0.389    -1.123274     .437335
       1221  |  -.7571391   .4291415    -1.76   0.078    -1.599077    .0847987
       1222  |  -.5118712   .4099169    -1.25   0.212    -1.316092    .2923497
       1223  |  -.7486499   .4019921    -1.86   0.063    -1.537323    .0400233
       1231  |   .1129776   .6011936     0.19   0.851    -1.066511    1.292466
       1232  |  -.2728563   .4648009    -0.59   0.557    -1.184755     .639042
       1233  |  -.3808538   .4081873    -0.93   0.351    -1.181681    .4199737
       1311  |  -.3306377   .4834437    -0.68   0.494    -1.279112    .6178362
       1312  |  -.5047283    .430494    -1.17   0.241     -1.34932    .3398629
       1313  |  -.3375258   .4149341    -0.81   0.416     -1.15159    .4765384
       1321  |  -.6836998   .3762225    -1.82   0.069    -1.421815    .0544157
       1322  |   -.466672   .3836574    -1.22   0.224    -1.219374      .28603
       1323  |  -.7537947   .3855899    -1.95   0.051    -1.510288    .0026987
       1331  |  -.4872442   .4509595    -1.08   0.280    -1.371987    .3974986
       1332  |  -.3318266   .4365959    -0.76   0.447    -1.188389    .5247361
       1333  |  -.4391696    .417351    -1.05   0.293    -1.257976    .3796363
       2010  |   .1013349   .5332765     0.19   0.849    -.9449067    1.147577
       2020  |  -.2294801   .6664533    -0.34   0.731    -1.537003    1.078043
       2030  |  -.1376943   .4436314    -0.31   0.756     -1.00806    .7326715
       3115  |  -.1346789   .3835294    -0.35   0.726    -.8871298     .617772
       3116  |  -.1159876   .3843249    -0.30   0.763    -.8699992     .638024
       3117  |  -.1619239   .3692361    -0.44   0.661    -.8863327    .5624848
       3120  |  -.4827288   .4758786    -1.01   0.311    -1.416361    .4509032
       3135  |  -.0733559   .4303363    -0.17   0.865    -.9176378    .7709259
       3136  |  -.4327972   .4829163    -0.90   0.370    -1.380236     .514642
       3137  |  -.4624215   .4303337    -1.07   0.283    -1.306698    .3818553
       3215  |  -.0868864   .3850277    -0.23   0.822    -.8422768     .668504
       3216  |  -.3748665    .387764    -0.97   0.334    -1.135625    .3858922
       3217  |   -.338438   .3855385    -0.88   0.380    -1.094831    .4179546
       3220  |  -.6831821   .4330199    -1.58   0.115    -1.532729    .1663649
       3235  |  -.2894093   .3950951    -0.73   0.464    -1.064551    .4857324
       3236  |  -.5045511   .4347165    -1.16   0.246    -1.357427    .3483244
       3237  |  -.0636366   .4832612    -0.13   0.895    -1.011753    .8844794
       3315  |   .1006811   .4492475     0.22   0.823     -.780703    .9820652
       3316  |  -.2045239   .4829835    -0.42   0.672    -1.152095    .7430471
       3317  |  -.7505398   .4492298    -1.67   0.095    -1.631889    .1308094
       3320  |   -.217775   .5362101    -0.41   0.685    -1.269772     .834222
       3330  |  -.4165118   .4268785    -0.98   0.329     -1.25401    .4209863
             |
       _cons |   .0218551   .3734877     0.06   0.953    -.7108949    .7546051
------------------------------------------------------------------------------

. lincom c.YH_wall_in2N 

 ( 1)  YH_wall_in2N = 0

------------------------------------------------------------------------------
don_C_wall~T | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6424546   .1238019     5.19   0.000     .3995661     .885343
------------------------------------------------------------------------------

. matrix EV3[2,3] = r(estimate)

. matrix EV3[2,4] = r(se)

. 
. 
. regress writing_minWNST c.abs_votes_minWw1LPP3N##i.method  i.block

      Source |       SS           df       MS      Number of obs   =     1,570
-------------+----------------------------------   F(45, 1524)     =      1.62
       Model |   71.740797        45  1.59423993   Prob > F        =    0.0062
    Residual |  1500.62935     1,524   .98466493   R-squared       =    0.0456
-------------+----------------------------------   Adj R-squared   =    0.0174
       Total |  1572.37015     1,569  1.00214796   Root MSE        =     .9923

------------------------------------------------------------------------------------------------
               writing_minWNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
         abs_votes_minWw1LPP3N |   .2610863   .1231541     2.12   0.034     .0195169    .5026556
                               |
                        method |
                      Likert+  |   .0290386   .1232543     0.24   0.814    -.2127273    .2708046
                         QVSR  |   .0262838   .1284706     0.20   0.838     -.225714    .2782816
                               |
method#c.abs_votes_minWw1LPP3N |
                      Likert+  |   .0944501    .175639     0.54   0.591    -.2500697    .4389699
                         QVSR  |   .2623062   .2678557     0.98   0.328    -.2630987    .7877111
                               |
                         block |
                         1120  |  -.4048902   .4095198    -0.99   0.323    -1.208172    .3983917
                         1130  |  -.1594502   .5014405    -0.32   0.751    -1.143037    .8241363
                         1210  |   .1685575   .3655795     0.46   0.645    -.5485347    .8856498
                         1221  |  -.0089019   .3956693    -0.02   0.982    -.7850158     .767212
                         1222  |  -.0966532   .3672679    -0.26   0.792    -.8170572    .6237508
                         1223  |   .1852978   .3624893     0.51   0.609    -.5257329    .8963284
                         1231  |  -.3292503   .5230143    -0.63   0.529    -1.355154    .6966536
                         1232  |  -.1993776    .431108    -0.46   0.644    -1.045005    .6462501
                         1233  |  -.2305782   .3723307    -0.62   0.536     -.960913    .4997566
                         1311  |   .5405534   .4465583     1.21   0.226    -.3353804    1.416487
                         1312  |   -.067797   .3986006    -0.17   0.865    -.8496607    .7140668
                         1313  |   -.026458   .3682313    -0.07   0.943    -.7487517    .6958356
                         1321  |  -.2679439   .3380089    -0.79   0.428    -.9309557     .395068
                         1322  |   .2101904   .3476718     0.60   0.546    -.4717754    .8921561
                         1323  |    .137674   .3454427     0.40   0.690    -.5399195    .8152674
                         1331  |  -.1040781   .3927867    -0.26   0.791    -.8745379    .6663816
                         1332  |  -.1420784   .3930227    -0.36   0.718    -.9130011    .6288442
                         1333  |  -.1936944   .3882358    -0.50   0.618    -.9552275    .5678386
                         2010  |  -.4292035   .4822861    -0.89   0.374    -1.375218    .5168111
                         2020  |   .8200104   .5537823     1.48   0.139    -.2662455    1.906266
                         2030  |  -.1881027   .4241921    -0.44   0.658    -1.020165    .6439593
                         3115  |   .0289815    .350183     0.08   0.934    -.6579101    .7158731
                         3116  |  -.0733566   .3470056    -0.21   0.833    -.7540156    .6073024
                         3117  |  -.0900881   .3376189    -0.27   0.790    -.7523349    .5721587
                         3120  |   .5716658   .4565139     1.25   0.211    -.3237961    1.467128
                         3135  |  -.0956244    .414223    -0.23   0.817    -.9081319    .7168831
                         3136  |  -.2817494   .4307373    -0.65   0.513     -1.12665    .5631513
                         3137  |   .1123911   .3909792     0.29   0.774    -.6545232    .8793053
                         3215  |  -.0851129   .3515884    -0.24   0.809    -.7747611    .6045353
                         3216  |  -.2420057   .3601767    -0.67   0.502       -.9485    .4644887
                         3217  |  -.1559525   .3553141    -0.44   0.661     -.852909    .5410039
                         3220  |  -.0655125   .3983929    -0.16   0.869    -.8469688    .7159438
                         3235  |  -.1979406    .363814    -0.54   0.586    -.9115697    .5156884
                         3236  |   -.299822   .4055773    -0.74   0.460    -1.095371    .4957267
                         3237  |  -.2182249   .4680568    -0.47   0.641    -1.136328    .6998787
                         3315  |   .2663103   .4092044     0.65   0.515    -.5363531    1.068974
                         3316  |   .1642501   .4462789     0.37   0.713    -.7111357    1.039636
                         3317  |  -.0429583     .43072    -0.10   0.921    -.8878249    .8019084
                         3320  |  -.1019494   .4559988    -0.22   0.823    -.9964009    .7925021
                         3330  |   .4162338    .424489     0.98   0.327    -.4164106    1.248878
                               |
                         _cons |  -.1327779   .3450605    -0.38   0.700    -.8096216    .5440658
------------------------------------------------------------------------------------------------

. lincom c.abs_votes_minWw1LPP3N + 1.method#c.abs_votes_minWw1LPP3N 

 ( 1)  abs_votes_minWw1LPP3N + 1b.method#co.abs_votes_minWw1LPP3N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2610863   .1231541     2.12   0.034     .0195169    .5026556
------------------------------------------------------------------------------

. matrix EV3[3,1] = r(estimate)

. matrix EV3[3,2] = r(se)

. lincom c.abs_votes_minWw1LPP3N + 3.method#c.abs_votes_minWw1LPP3N 

 ( 1)  abs_votes_minWw1LPP3N + 3.method#c.abs_votes_minWw1LPP3N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .5233925   .2385803     2.19   0.028      .055412     .991373
------------------------------------------------------------------------------

. matrix EV3[3,5] = r(estimate)

. matrix EV3[3,6] = r(se)

. regress writing_minWNST c.YH_minW2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       542
-------------+----------------------------------   F(41, 500)      =      1.02
       Model |  57.2528039        41  1.39640985   Prob > F        =    0.4432
    Residual |  685.860492       500  1.37172098   R-squared       =    0.0770
-------------+----------------------------------   Adj R-squared   =    0.0014
       Total |  743.113296       541  1.37359204   Root MSE        =    1.1712

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   YH_minW2N |   .7361902   .2952893     2.49   0.013     .1560294    1.316351
             |
       block |
       1120  |  -.4156803   .8284065    -0.50   0.616    -2.043267    1.211906
       1130  |  -.5441573   1.070868    -0.51   0.612    -2.648113    1.559798
       1210  |   .0301981   .7502087     0.04   0.968    -1.443752    1.504148
       1221  |  -.2663229   .8553351    -0.31   0.756    -1.946817    1.414171
       1222  |   .0351789   .7453461     0.05   0.962    -1.429217    1.499575
       1223  |   .4650888   .7502664     0.62   0.536    -1.008974    1.939152
       1231  |  -.4163903   1.069199    -0.39   0.697    -2.517066    1.684286
       1232  |  -.1040756   .8951015    -0.12   0.907    -1.862699    1.654548
       1233  |  -.0876637   .7408386    -0.12   0.906    -1.543204    1.367877
       1311  |  -.1313377    .895591    -0.15   0.883    -1.890923    1.628248
       1312  |  -.1945321   .8086633    -0.24   0.810    -1.783329    1.394265
       1313  |  -.1089205   .7631104    -0.14   0.887    -1.608219    1.390378
       1321  |  -.2565995    .692935    -0.37   0.711    -1.618023    1.104824
       1322  |   .3219989   .7024349     0.46   0.647    -1.058089    1.702087
       1323  |   .0263139   .7025177     0.04   0.970    -1.353937    1.406564
       1331  |   .1961502   .7713902     0.25   0.799    -1.319415    1.711716
       1332  |   .1464276   .7940732     0.18   0.854    -1.413704    1.706559
       1333  |  -.0555129   .7808481    -0.07   0.943    -1.589661    1.478635
       2010  |  -.4935442   1.069913    -0.46   0.645    -2.595624    1.608536
       2020  |   2.849686   1.069862     2.66   0.008     .7477075    4.951665
       2030  |   .2248493   .8553877     0.26   0.793    -1.455748    1.905447
       3115  |   .3864775   .7115453     0.54   0.587     -1.01151    1.784465
       3116  |  -.0262969    .707439    -0.04   0.970    -1.416216    1.363623
       3117  |    .011507    .691634     0.02   0.987     -1.34736    1.370374
       3120  |   1.112539    .958195     1.16   0.246    -.7700456    2.995124
       3135  |   .0789997   .8297277     0.10   0.924    -1.551183    1.709182
       3136  |  -.3194083    .896699    -0.36   0.722    -2.081171    1.442354
       3137  |   .0984869   .7955206     0.12   0.902    -1.464488    1.661462
       3215  |   .0459385    .718976     0.06   0.949    -1.366648    1.458525
       3216  |  -.1111819   .7254984    -0.15   0.878    -1.536583    1.314219
       3217  |   .0695061   .7213429     0.10   0.923    -1.347731    1.486743
       3220  |  -.0124859   .8084249    -0.02   0.988    -1.600814    1.575843
       3235  |  -.2515963   .7407518    -0.34   0.734    -1.706966    1.203773
       3236  |   .0523446    .896434     0.06   0.953    -1.708897    1.813586
       3237  |  -.4098778   .9580333    -0.43   0.669    -2.292145    1.472389
       3315  |    .259901   .8083126     0.32   0.748    -1.328207    1.848009
       3316  |  -.0332651   .8553673    -0.04   0.969    -1.713822    1.647292
       3317  |   .1928827   .8563356     0.23   0.822    -1.489577    1.875342
       3320  |   .0220336   .9563277     0.02   0.982    -1.856882     1.90095
       3330  |    1.11724    .828837     1.35   0.178    -.5111923    2.745673
             |
       _cons |  -.3288013   .6852839    -0.48   0.632    -1.675192    1.017589
------------------------------------------------------------------------------

. lincom c.YH_minW2N

 ( 1)  YH_minW2N = 0

------------------------------------------------------------------------------
writing~WNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7361902   .2952893     2.49   0.013     .1560294    1.316351
------------------------------------------------------------------------------

. matrix EV3[4,3] = r(estimate)

. matrix EV3[4,4] = r(se)

. 
. 
. regress writing_abortionNST c.abs_votes_abortion_inw1LPP3N##i.method  i.block, 

      Source |       SS           df       MS      Number of obs   =     1,569
-------------+----------------------------------   F(45, 1523)     =      3.31
       Model |   140.19987        45  3.11555266   Prob > F        =    0.0000
    Residual |  1431.73637     1,523  .940076405   R-squared       =    0.0892
-------------+----------------------------------   Adj R-squared   =    0.0623
       Total |  1571.93623     1,568  1.00251035   Root MSE        =    .96958

-------------------------------------------------------------------------------------------------------
                  writing_abortionNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------------------------------+----------------------------------------------------------------
         abs_votes_abortion_inw1LPP3N |   .3837219   .1162126     3.30   0.001     .1557683    .6116756
                                      |
                               method |
                             Likert+  |  -.1266345   .1258656    -1.01   0.315    -.3735228    .1202537
                                QVSR  |  -.2570649   .1309017    -1.96   0.050    -.5138316   -.0002982
                                      |
method#c.abs_votes_abortion_inw1LPP3N |
                             Likert+  |   .3605658   .1631984     2.21   0.027     .0404484    .6806833
                                QVSR  |   .6530014    .217334     3.00   0.003     .2266958    1.079307
                                      |
                                block |
                                1120  |  -.0394613   .3999922    -0.10   0.921     -.824055    .7451325
                                1130  |   .1122229   .4888311     0.23   0.818    -.8466304    1.071076
                                1210  |   .2603934   .3573776     0.73   0.466    -.4406108    .9613977
                                1221  |   .1590405   .3870899     0.41   0.681    -.6002452    .9183262
                                1222  |   .1167706   .3589129     0.33   0.745    -.5872453    .8207865
                                1223  |   .3752736   .3542866     1.06   0.290    -.3196675    1.070215
                                1231  |    .590805   .5119242     1.15   0.249    -.4133461    1.594956
                                1232  |   .1844716   .4214531     0.44   0.662    -.6422183    1.011162
                                1233  |   .4146649   .3641234     1.14   0.255    -.2995715    1.128901
                                1311  |   .5141573   .4362992     1.18   0.239    -.3416536    1.369968
                                1312  |   .5164363   .3896053     1.33   0.185    -.2477834    1.280656
                                1313  |   .3769042   .3597909     1.05   0.295    -.3288338    1.082642
                                1321  |   .5227243   .3304035     1.58   0.114    -.1253697    1.170818
                                1322  |   .2795255   .3398095     0.82   0.411    -.3870186    .9460696
                                1323  |    .416637   .3376886     1.23   0.217    -.2457469    1.079021
                                1331  |   .4599422   .3837642     1.20   0.231      -.29282    1.212704
                                1332  |   .1960915   .3840627     0.51   0.610    -.5572563    .9494393
                                1333  |   .4962335    .379317     1.31   0.191    -.2478054    1.240272
                                2010  |   .6149219   .4718874     1.30   0.193     -.310696     1.54054
                                2020  |   .5770042   .5409451     1.07   0.286     -.484072     1.63808
                                2030  |   .0686286    .414461     0.17   0.869    -.7443462    .8816034
                                3115  |   .6250333   .3424638     1.83   0.068    -.0467173    1.296784
                                3116  |   .5000896   .3393832     1.47   0.141    -.1656182    1.165797
                                3117  |   .3211441   .3300692     0.97   0.331    -.3262942    .9685824
                                3120  |   .3750294   .4456037     0.84   0.400    -.4990324    1.249091
                                3135  |  -.0141487    .404595    -0.03   0.972     -.807771    .7794736
                                3136  |   .4552114   .4205997     1.08   0.279    -.3698046    1.280227
                                3137  |  -.0490577   .3814782    -0.13   0.898    -.7973359    .6992205
                                3215  |   .2958093    .343618     0.86   0.389    -.3782053    .9698239
                                3216  |   .4621886   .3521129     1.31   0.190     -.228489    1.152866
                                3217  |   .2138551   .3473056     0.62   0.538    -.4673928    .8951029
                                3220  |  -.0834349   .3893766    -0.21   0.830     -.847206    .6803361
                                3235  |   .1008648   .3558703     0.28   0.777    -.5971828    .7989125
                                3236  |   .2069867   .3964076     0.52   0.602    -.5705758    .9845492
                                3237  |   .2834078   .4574377     0.62   0.536    -.6138668    1.180682
                                3315  |   .1074453    .400147     0.27   0.788    -.6774522    .8923428
                                3316  |   .3459978   .4363241     0.79   0.428    -.5098619    1.201857
                                3317  |  -.1922795   .4206439    -0.46   0.648    -1.017382    .6328231
                                3320  |   .1761001   .4457963     0.40   0.693    -.6983396     1.05054
                                3330  |   .4691635   .4153612     1.13   0.259     -.345577    1.283904
                                      |
                                _cons |  -.6057633   .3391609    -1.79   0.074    -1.271035    .0595086
-------------------------------------------------------------------------------------------------------

. lincom c.abs_votes_abortion_inw1LPP3N + 1.method#c.abs_votes_abortion_inw1LPP3N 

 ( 1)  abs_votes_abortion_inw1LPP3N + 1b.method#co.abs_votes_abortion_inw1LPP3N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3837219   .1162126     3.30   0.001     .1557683    .6116756
------------------------------------------------------------------------------

. matrix EV3[4,1] = r(estimate)

. matrix EV3[4,2] = r(se)

. lincom c.abs_votes_abortion_inw1LPP3N + 3.method#c.abs_votes_abortion_inw1LPP3N 

 ( 1)  abs_votes_abortion_inw1LPP3N + 3.method#c.abs_votes_abortion_inw1LPP3N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.036723   .1867561     5.55   0.000      .670397     1.40305
------------------------------------------------------------------------------

. matrix EV3[4,5] = r(estimate)

. matrix EV3[4,6] = r(se)

. regress writing_abortionNST c.YH_abortion_in2N i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       541
-------------+----------------------------------   F(41, 499)      =      2.03
       Model |  85.1439827        41  2.07668251   Prob > F        =    0.0003
    Residual |  509.711087       499   1.0214651   R-squared       =    0.1431
-------------+----------------------------------   Adj R-squared   =    0.0727
       Total |   594.85507       540  1.10158346   Root MSE        =    1.0107

----------------------------------------------------------------------------------
writing_abor~NST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
YH_abortion_in2N |   1.093162   .1729059     6.32   0.000     .7534487    1.432875
                 |
           block |
           1120  |  -.4805155   .7148315    -0.67   0.502    -1.884966    .9239349
           1130  |   -.608964   .9226271    -0.66   0.510    -2.421677    1.203749
           1210  |   .3997948      .6475     0.62   0.537    -.8723675    1.671957
           1221  |  -.2714372   .7385082    -0.37   0.713    -1.722406    1.179532
           1222  |   -.263401   .6430591    -0.41   0.682    -1.526838    1.000036
           1223  |   .1726114   .6479493     0.27   0.790    -1.100434    1.445657
           1231  |   .7592445   .9228973     0.82   0.411    -1.053999    2.572488
           1232  |   .4912476   .7720586     0.64   0.525    -1.025639    2.008134
           1233  |   .3940764   .6397813     0.62   0.538    -.8629206    1.651073
           1311  |   1.030107   .7719166     1.33   0.183    -.4864998    2.546715
           1312  |    .225143   .6974953     0.32   0.747    -1.145246    1.595532
           1313  |  -.0264551   .6582956    -0.04   0.968    -1.319828    1.266918
           1321  |   .1721746   .5980314     0.29   0.774    -1.002795    1.347144
           1322  |  -.0811981   .6062014    -0.13   0.893     -1.27222    1.109823
           1323  |   .2420927   .6063817     0.40   0.690    -.9492834    1.433469
           1331  |   .2411513   .6654855     0.36   0.717    -1.066348     1.54865
           1332  |   .0444375   .6842544     0.06   0.948    -1.299937    1.388812
           1333  |   .2098068   .6739136     0.31   0.756    -1.114251    1.533865
           2010  |   2.061033   .9226247     2.23   0.026     .2483249    3.873741
           2020  |  -.5970637   .9226199    -0.65   0.518    -2.409762    1.215635
           2030  |  -.0200625   .7381316    -0.03   0.978    -1.470291    1.430166
           3115  |   .0868672   .6141173     0.14   0.888    -1.119707    1.293442
           3116  |   .3795519   .6102534     0.62   0.534    -.8194308    1.578535
           3117  |   .1425814   .5961945     0.24   0.811    -1.028779    1.313942
           3120  |  -.3450895   .8253306    -0.42   0.676    -1.966641    1.276462
           3135  |  -.3313315   .7146559    -0.46   0.643    -1.735437    1.072774
           3136  |  -.0963363   .7722837    -0.12   0.901    -1.613665    1.420992
           3137  |  -.2947206   .6843158    -0.43   0.667    -1.639216    1.049775
           3215  |  -.2256111   .6204181    -0.36   0.716    -1.444565    .9933424
           3216  |    .015833   .6260827     0.03   0.980     -1.21425    1.245916
           3217  |  -.0636466   .6223454    -0.10   0.919    -1.286387    1.159094
           3220  |  -.2026148   .6979143    -0.29   0.772    -1.573827    1.168598
           3235  |  -.1086762   .6395795    -0.17   0.865    -1.365277    1.147924
           3236  |  -.2500922   .7736862    -0.32   0.747    -1.770176    1.269992
           3237  |  -.3227891   .8253089    -0.39   0.696    -1.944298    1.298719
           3315  |   .0298228   .6987974     0.04   0.966    -1.343125    1.402771
           3316  |   .4673018   .7389761     0.63   0.527    -.9845863     1.91919
           3317  |   -.596997   .7380975    -0.81   0.419    -2.047159    .8531649
           3320  |  -.3474505   .8253246    -0.42   0.674     -1.96899    1.274089
           3330  |   .0680284    .715658     0.10   0.924    -1.338046    1.474103
                 |
           _cons |   -.742918   .5961584    -1.25   0.213    -1.914208    .4283718
----------------------------------------------------------------------------------

. lincom c.YH_abortion_in2N 

 ( 1)  YH_abortion_in2N = 0

------------------------------------------------------------------------------
writing~nNST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.093162   .1729059     6.32   0.000     .7534487    1.432875
------------------------------------------------------------------------------

. matrix EV3[3,3] = r(estimate)

. matrix EV3[3,4] = r(se)

. 
. 
. regress punish_FaST c.diffLPP3N##i.method  i.block

      Source |       SS           df       MS      Number of obs   =     1,542
-------------+----------------------------------   F(45, 1496)     =      1.34
       Model |  59.8682549        45  1.33040567   Prob > F        =    0.0668
    Residual |  1484.73865     1,496  .992472359   R-squared       =    0.0388
-------------+----------------------------------   Adj R-squared   =    0.0098
       Total |   1544.6069     1,541  1.00234063   Root MSE        =    .99623

------------------------------------------------------------------------------------
       punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
         diffLPP3N |   .2536402   .2361063     1.07   0.283    -.2094943    .7167748
                   |
            method |
          Likert+  |  -.1763746   .2003305    -0.88   0.379    -.5693331    .2165838
             QVSR  |  -.1999328   .1998987    -1.00   0.317    -.5920442    .1921787
                   |
method#c.diffLPP3N |
          Likert+  |   .2935067   .3430128     0.86   0.392    -.3793305    .9663438
             QVSR  |   .4789939   .3459769     1.38   0.166    -.1996575    1.157645
                   |
             block |
             1120  |  -.3310659   .4110756    -0.81   0.421    -1.137412    .4752799
             1130  |  -.1477843    .502927    -0.29   0.769    -1.134301    .8387327
             1210  |  -.0949166   .3663452    -0.26   0.796    -.8135213    .6236881
             1221  |   -.395439   .4002302    -0.99   0.323    -1.180511    .3896328
             1222  |   .0588306    .371182     0.16   0.874    -.6692618     .786923
             1223  |   .2651306   .3628102     0.73   0.465    -.4465401    .9768014
             1231  |    .444459   .5262596     0.84   0.398    -.5878261    1.476744
             1232  |  -.1137695   .4329223    -0.26   0.793    -.9629686    .7354296
             1233  |  -.1714469   .3739315    -0.46   0.647    -.9049326    .5620388
             1311  |   -.311601   .4483194    -0.70   0.487    -1.191002    .5678003
             1312  |  -.3167187    .400373    -0.79   0.429    -1.102071    .4686334
             1313  |  -.2709482   .3696219    -0.73   0.464    -.9959804     .454084
             1321  |  -.0086417   .3398048    -0.03   0.980    -.6751863    .6579028
             1322  |   .0122505   .3490151     0.04   0.972    -.6723603    .6968614
             1323  |   .2081515   .3473848     0.60   0.549    -.4732614    .8895645
             1331  |  -.1770432   .3970649    -0.45   0.656    -.9559062    .6018199
             1332  |  -.1806845   .4005124    -0.45   0.652    -.9663101     .604941
             1333  |  -.2067935    .389648    -0.53   0.596    -.9711079    .5575209
             2010  |  -.4178771    .484671    -0.86   0.389    -1.368584    .5328298
             2020  |   .6906075   .5565448     1.24   0.215    -.4010835    1.782299
             2030  |  -.0278757   .4257515    -0.07   0.948    -.8630089    .8072576
             3115  |   .2455593   .3515372     0.70   0.485    -.4439987    .9351174
             3116  |   .0234181   .3490133     0.07   0.947    -.6611892    .7080255
             3117  |  -.0349798   .3392387    -0.10   0.918    -.7004138    .6304541
             3120  |  -.2956747   .4492767    -0.66   0.511    -1.176954    .5856044
             3135  |  -.0710223   .4158103    -0.17   0.864    -.8866554    .7446108
             3136  |   .0162756   .4397711     0.04   0.970    -.8463578    .8789091
             3137  |   .0526642   .3922159     0.13   0.893    -.7166873    .8220157
             3215  |   .2448557   .3537594     0.69   0.489    -.4490613    .9387728
             3216  |  -.1668231   .3621486    -0.46   0.645     -.877196    .5435498
             3217  |   .1518514   .3589854     0.42   0.672    -.5523169    .8560196
             3220  |  -.0043296   .4041908    -0.01   0.991    -.7971705    .7885112
             3235  |   .0143916    .366056     0.04   0.969    -.7036459    .7324291
             3236  |  -.1469779   .4069835    -0.36   0.718    -.9452967     .651341
             3237  |  -.3784913   .4703257    -0.80   0.421    -1.301059    .5440765
             3315  |    .279878   .4072554     0.69   0.492    -.5189743     1.07873
             3316  |   .2401028   .4480206     0.54   0.592    -.6387125    1.118918
             3317  |  -.1247793   .4327177    -0.29   0.773    -.9735771    .7240184
             3320  |  -.1288703   .4480445    -0.29   0.774    -1.007732    .7499918
             3330  |   .3805917   .4396367     0.87   0.387    -.4817781    1.242962
                   |
             _cons |   -.158663   .3588455    -0.44   0.658    -.8625568    .5452309
------------------------------------------------------------------------------------

. lincom c.diffLPP3N + 1.method#c.diffLPP3N 

 ( 1)  diffLPP3N + 1b.method#co.diffLPP3N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2536402   .2361063     1.07   0.283    -.2094943    .7167748
------------------------------------------------------------------------------

. matrix EV3[5,1] = r(estimate)

. matrix EV3[5,2] = r(se)

. lincom c.diffLPP3N + 3.method#c.diffLPP3N 

 ( 1)  diffLPP3N + 3.method#c.diffLPP3N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7326341   .2588086     2.83   0.005     .2249679      1.2403
------------------------------------------------------------------------------

. matrix EV3[5,5] = r(estimate)

. matrix EV3[5,6] = r(se)

. regress punish_FaST c.diffYH2N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       534
-------------+----------------------------------   F(41, 492)      =      1.35
       Model |  55.7543741        41  1.35986278   Prob > F        =    0.0758
    Residual |  494.591203       492  1.00526667   R-squared       =    0.1013
-------------+----------------------------------   Adj R-squared   =    0.0264
       Total |  550.345577       533   1.0325433   Root MSE        =    1.0026

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    diffYH2N |   .7841936   .2557181     3.07   0.002     .2817593    1.286628
             |
       block |
       1120  |  -1.414652   .7091306    -1.99   0.047     -2.80795    -.021354
       1130  |  -1.536169   .9202337    -1.67   0.096    -3.344242    .2719039
       1210  |  -1.055675   .6381306    -1.65   0.099    -2.309472    .1981222
       1221  |  -1.306625   .7325066    -1.78   0.075    -2.745852    .1326015
       1222  |   -1.07597   .6398106    -1.68   0.093    -2.333069    .1811279
       1223  |  -.7587142   .6344447    -1.20   0.232    -2.005269     .487841
       1231  |  -.5102494   .9175778    -0.56   0.578    -2.313104    1.292605
       1232  |  -1.321919    .766198    -1.73   0.085    -2.827343    .1835046
       1233  |  -1.056535   .6342327    -1.67   0.096    -2.302673    .1896042
       1311  |  -1.238721   .7657728    -1.62   0.106     -2.74331     .265867
       1312  |  -1.209037   .6919716    -1.75   0.081    -2.568621    .1505475
       1313  |  -1.271826   .6531172    -1.95   0.052    -2.555069    .0114171
       1321  |  -.8205658   .5942264    -1.38   0.168      -1.9881    .3469688
       1322  |  -.7512402   .6012909    -1.25   0.212    -1.932655    .4301747
       1323  |  -.9453672   .6018806    -1.57   0.117    -2.127941    .2372062
       1331  |  -1.218754   .6600245    -1.85   0.065    -2.515568    .0780605
       1332  |  -1.286256   .6685653    -1.92   0.055    -2.599852    .0273389
       1333  |  -1.141303   .6684589    -1.71   0.088    -2.454689    .1720834
       2010  |  -1.290011   .9153973    -1.41   0.159    -3.088581    .5085594
       2020  |   .4466478   .9170511     0.49   0.626    -1.355172    2.248467
       2030  |  -.3360344   .7322705    -0.46   0.647    -1.774798    1.102729
       3115  |  -.4153902   .6102434    -0.68   0.496    -1.614395    .7836144
       3116  |  -.8757835   .6062581    -1.44   0.149    -2.066958    .3153908
       3117  |  -.9587708   .5913261    -1.62   0.106    -2.120607    .2030652
       3120  |   -1.24499    .818644    -1.52   0.129     -2.85346    .3634795
       3135  |  -.5867777   .7090581    -0.83   0.408    -1.979933    .8063779
       3136  |  -.1389679   .8198971    -0.17   0.865      -1.7499    1.471964
       3137  |  -1.425111   .6793947    -2.10   0.036    -2.759984    -.090238
       3215  |  -.8973624   .6154642    -1.46   0.145    -2.106625    .3119002
       3216  |  -1.181692    .622938    -1.90   0.058    -2.405639     .042255
       3217  |  -1.109984   .6188429    -1.79   0.073    -2.325885    .1059173
       3220  |  -1.426919   .6944606    -2.05   0.040    -2.791393   -.0624445
       3235  |  -1.087399   .6342034    -1.71   0.087     -2.33348    .1586825
       3236  |  -.8953776   .7658143    -1.17   0.243    -2.400048    .6092924
       3237  |  -1.298439   .8188398    -1.59   0.113    -2.907293    .3104155
       3315  |  -.6039212   .6927539    -0.87   0.384    -1.965042    .7571998
       3316  |  -.1474119    .732256    -0.20   0.841    -1.586146    1.291323
       3317  |  -.9381248   .7660891    -1.22   0.221    -2.443335     .567085
       3320  |  -.9422078   .7666852    -1.23   0.220    -2.448589    .5641733
       3330  |   .3567046   .7322983     0.49   0.626    -1.082113    1.795522
             |
       _cons |   .4709164   .5941827     0.79   0.428    -.6965323    1.638365
------------------------------------------------------------------------------

. lincom c.diffYH2N

 ( 1)  diffYH2N = 0

------------------------------------------------------------------------------
 punish_FaST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7841936   .2557181     3.07   0.002     .2817593    1.286628
------------------------------------------------------------------------------

. matrix EV3[5,3] = r(estimate)

. matrix EV3[5,4] = r(se)

. 
. 
. regress proportionST c.diffLPP3N##i.method i.block

      Source |       SS           df       MS      Number of obs   =     1,521
-------------+----------------------------------   F(45, 1475)     =      1.97
       Model |  86.2915936        45  1.91759097   Prob > F        =    0.0002
    Residual |  1436.32402     1,475  .973778998   R-squared       =    0.0567
-------------+----------------------------------   Adj R-squared   =    0.0279
       Total |  1522.61562     1,520   1.0017208   Root MSE        =     .9868

------------------------------------------------------------------------------------
      proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
         diffLPP3N |   .4737162   .2347876     2.02   0.044      .013163    .9342694
                   |
            method |
          Likert+  |  -.2144004   .1996092    -1.07   0.283    -.6059485    .1771477
             QVSR  |  -.3846788   .2000536    -1.92   0.055    -.7770987    .0077411
                   |
method#c.diffLPP3N |
          Likert+  |   .3384122   .3416164     0.99   0.322    -.3316935    1.008518
             QVSR  |   .9229285   .3459192     2.67   0.008     .2443825    1.601474
                   |
             block |
             1120  |  -.4248264   .4277862    -0.99   0.321     -1.26396    .4143077
             1130  |  -.6414147   .5117869    -1.25   0.210    -1.645322     .362493
             1210  |  -.2329446   .3816317    -0.61   0.542    -.9815433     .515654
             1221  |  -.7774368   .4132751    -1.88   0.060    -1.588106    .0332328
             1222  |  -.3241998   .3858438    -0.84   0.401    -1.081061    .4326613
             1223  |  -.1741882   .3781795    -0.46   0.645    -.9160152    .5676388
             1231  |   .3274323   .5348618     0.61   0.541    -.7217386    1.376603
             1232  |  -.3531328   .4445017    -0.79   0.427    -1.225056      .51879
             1233  |  -.5570318   .3885145    -1.43   0.152    -1.319132     .205068
             1311  |  -.6007964   .4592061    -1.31   0.191    -1.501563    .2999702
             1312  |  -.6067617   .4167573    -1.46   0.146    -1.424262    .2107383
             1313  |  -.6145623   .3846542    -1.60   0.110     -1.36909    .1399652
             1321  |  -.3931134   .3564842    -1.10   0.270    -1.092383    .3061565
             1322  |  -.2970405   .3650759    -0.81   0.416    -1.013164    .4190828
             1323  |   -.242392   .3639173    -0.67   0.505    -.9562426    .4714585
             1331  |     -.5807   .4103776    -1.42   0.157    -1.385686    .2242858
             1332  |  -.6251471   .4138716    -1.51   0.131    -1.436987    .1866926
             1333  |  -.3882454   .4031719    -0.96   0.336    -1.179097    .4026058
             2010  |  -.2470078   .5124864    -0.48   0.630    -1.252288     .758272
             2020  |  -.0597069    .563671    -0.11   0.916    -1.165389    1.045975
             2030  |  -.2652684   .4377343    -0.61   0.545    -1.123916    .5933797
             3115  |  -.0131723   .3681725    -0.04   0.971    -.7353697     .709025
             3116  |  -.2051365   .3655646    -0.56   0.575    -.9222185    .5119455
             3117  |  -.2208954   .3562818    -0.62   0.535    -.9197685    .4779776
             3120  |  -.5671415   .4608721    -1.23   0.219    -1.471176    .3368932
             3135  |   -.483202   .4283663    -1.13   0.259    -1.323474      .35707
             3136  |   -.348919   .4510459    -0.77   0.439    -1.233679    .5358406
             3137  |   .0085402   .4110444     0.02   0.983    -.7977535     .814834
             3215  |  -.0551891    .369783    -0.15   0.881    -.7805456    .6701673
             3216  |  -.3817873   .3781016    -1.01   0.313    -1.123461    .3598867
             3217  |  -.0608592   .3760499    -0.16   0.871    -.7985088    .6767904
             3220  |  -.4558929   .4171057    -1.09   0.275    -1.274076    .3622906
             3235  |  -.2708969   .3810806    -0.71   0.477    -1.018415    .4766207
             3236  |  -.5011749   .4198825    -1.19   0.233    -1.324805    .3224556
             3237  |  -.7760445   .4806132    -1.61   0.107    -1.718803    .1667136
             3315  |  -.0508606   .4204161    -0.12   0.904    -.8755378    .7738165
             3316  |  -.1006171   .4591636    -0.22   0.827      -1.0013    .8000661
             3317  |  -.2229939   .4446855    -0.50   0.616    -1.095277    .6492895
             3320  |  -.2287176   .4585988    -0.50   0.618    -1.128293    .6708577
             3330  |   .0717336   .4509493     0.16   0.874    -.8128366    .9563038
                   |
             _cons |  -.0000165   .3744815    -0.00   1.000    -.7345896    .7345566
------------------------------------------------------------------------------------

. lincom c.diffLPP3N + 1.method#c.diffLPP3N 

 ( 1)  diffLPP3N + 1b.method#co.diffLPP3N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4737162   .2347876     2.02   0.044      .013163    .9342694
------------------------------------------------------------------------------

. matrix EV3[6,1] = r(estimate)

. matrix EV3[6,2] = r(se)

. lincom c.diffLPP3N + 3.method#c.diffLPP3N 

 ( 1)  diffLPP3N + 3.method#c.diffLPP3N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.396645   .2600475     5.37   0.000     .8865424    1.906747
------------------------------------------------------------------------------

. matrix EV3[6,5] = r(estimate)

. matrix EV3[6,6] = r(se)

. regress proportionST c.diffYH2N  i.block if method == 2

      Source |       SS           df       MS      Number of obs   =       528
-------------+----------------------------------   F(41, 486)      =      1.53
       Model |  50.9949268        41   1.2437787   Prob > F        =    0.0209
    Residual |  394.286681       486  .811289467   R-squared       =    0.1145
-------------+----------------------------------   Adj R-squared   =    0.0398
       Total |  445.281607       527  .844936637   Root MSE        =    .90072

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    diffYH2N |   .9404074   .2306242     4.08   0.000     .4872639    1.393551
             |
       block |
       1120  |  -1.053358   .6370511    -1.65   0.099    -2.305073    .1983561
       1130  |  -1.237399     .82673    -1.50   0.135    -2.861805    .3870075
       1210  |  -.6595044   .5732686    -1.15   0.251    -1.785895    .4668865
       1221  |  -.9621297   .6580519    -1.46   0.144    -2.255108    .3308483
       1222  |  -.6890818   .5747897    -1.20   0.231    -1.818461    .4402979
       1223  |  -.4588941   .5699579    -0.81   0.421     -1.57878    .6609918
       1231  |   .2041289   .8243255     0.25   0.805    -1.415553    1.823811
       1232  |    -.98047   .6883197    -1.42   0.155     -2.33292    .3719798
       1233  |  -.6292506    .569766    -1.10   0.270    -1.748759    .4902582
       1311  |   -.880699   .6879347    -1.28   0.201    -2.232392    .4709945
       1312  |  -.8451009   .6216358    -1.36   0.175    -2.066526    .3763247
       1313  |  -.9203979   .5867306    -1.57   0.117     -2.07324    .2324439
       1321  |  -.4694624   .5340796    -0.88   0.380    -1.518853    .5799278
       1322  |   -.464383   .5401719    -0.86   0.390    -1.525744    .5969776
       1323  |  -.3810547    .541264    -0.70   0.482    -1.444561    .6824519
       1331  |   -.768525   .5929354    -1.30   0.196    -1.933558    .3965084
       1332  |  -.9377031    .600609    -1.56   0.119    -2.117814    .2424078
       1333  |  -.3220389   .6005127    -0.54   0.592    -1.501961    .8578828
       2010  |  -.9422054   .8223513    -1.15   0.252    -2.558008    .6735974
       2020  |   .2332316   .8238486     0.28   0.777    -1.385513    1.851976
       2030  |    .422493   .6578382     0.64   0.521    -.8700651    1.715051
       3115  |   .0025247   .5482148     0.00   0.996    -1.074639    1.079688
       3116  |  -.4353699   .5454442    -0.80   0.425     -1.50709    .6363501
       3117  |  -.5075786     .53122    -0.96   0.340     -1.55135    .5361927
       3120  |  -.8882164   .7354317    -1.21   0.228    -2.333235    .5568019
       3135  |  -.4012758   .6369855    -0.63   0.529    -1.652861    .8503097
       3136  |  -.1666507   .7365663    -0.23   0.821    -1.613898    1.280597
       3137  |  -.7143686   .6373183    -1.12   0.263    -1.966608    .5378708
       3215  |  -.5869664   .5529045    -1.06   0.289    -1.673345    .4994119
       3216  |  -.7194364   .5596188    -1.29   0.199    -1.819007    .3801346
       3217  |  -.4859569    .557676    -0.87   0.384    -1.581711    .6097968
       3220  |  -1.106386   .6238893    -1.77   0.077    -2.332239    .1194673
       3235  |  -.6267448   .5697394    -1.10   0.272    -1.746201    .4927117
       3236  |  -.2514978   .6879723    -0.37   0.715    -1.603265     1.10027
       3237  |  -.9523123    .735609    -1.29   0.196    -2.397679    .4930544
       3315  |  -.3786358   .6223441    -0.61   0.543    -1.601453    .8441813
       3316  |   .1496318    .657825     0.23   0.820      -1.1429    1.442164
       3317  |  -.3027605   .6882211    -0.44   0.660    -1.655017    1.049496
       3320  |  -.0839746   .6887608    -0.12   0.903    -1.437291    1.269342
       3330  |   .4832632   .6578633     0.73   0.463    -.8093443    1.775871
             |
       _cons |  -.0595663   .5338926    -0.11   0.911    -1.108589    .9894565
------------------------------------------------------------------------------

. lincom c.diffYH2N

 ( 1)  diffYH2N = 0

------------------------------------------------------------------------------
proportionST | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9404074   .2306242     4.08   0.000     .4872639    1.393551
------------------------------------------------------------------------------

. matrix EV3[6,3] = r(estimate)

. matrix EV3[6,4] = r(se)

. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. 
. putexcel set  "$pathtemp/EV3", replace
note: file will be replaced when the first putexcel command is issued.

. putexcel A1=matrix(EV3) 
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp/EV3.xlsx saved

. 
. 
. 
. 
. **** code for the figure 
. 
. 
. preserve

. 
. clear all

. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. import excel EV3
(6 vars, 6 obs)

. 
. gen topic = _n

. 
. 
. rename A est1

. rename B se1

. rename C est2

. rename D se2

. rename E est3

. rename F se3

. 
. reshape long est se, i(topic) j(method)    
(j = 1 2 3)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations                6   ->   18          
Number of variables                   7   ->   4           
j variable (3 values)                     ->   method
xij variables:
                         est1 est2 est3   ->   est
                            se1 se2 se3   ->   se
-----------------------------------------------------------------------------

. 
. sort topic method

. gen order = _n

. gen lb = est - 1.96*se

. gen ub = est + 1.96*se

. 
. 
. tw (scatter est order,  legend(off) ylabel(0(0.2)2.2) yline(0, lc(black) lstyle(gs15)) ///
> text(2.2 1.5 "Gun", place(e)) ///
> text(2.2 3.9 "Immigration", place(e)) ///
> text(2.2 7.2 "Minimum", place(e)) ///
> text(2.0 7.4 "Wage", place(e)) ///
> text(2.2 10 "Abortion", place(e)) ///
> text(2.2 12.5 "DG punish (1)", place(e)) ///
> text(2.2 15.8 "DG punish (2)", place(e)) ///
> xlabel(1 "Likert" 2 "Likert +" 3 "QVSR" 4 " " 5 " " 6 " " 7 "Likert" 8 "Likert +" 9 "QVSR" ///
> 10 " " 11 " " 12 " " 13 "Likert" 14 "Likert +" 15 "QVSR" 16 " " 17 " " 18 " " , angle(45)labsize(small) ) ///
> xtitle(" ", size(zero))) ///
> (rcap lb ub order, xline(3.5) xline(6.5) xline(9.5) xline(12.5) xline(15.5))
(note:  named style gs15 not found in class linestyle, default attributes used)

. 
. cd "$pathfig"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig

. graph export "FigE3.pdf", replace 
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/FigE3.pdf saved as PDF format

. 
. restore

. 
. 
. erase "$pathtemp/EV.xlsx"

. erase "$pathtemp/EV2.xlsx"

. erase "$pathtemp/EV3.xlsx"

. 
. 
. 
. *****************************
. * OUTPUT FIG E3             *
. * SEE "FigE3t.pdf" IN "FIG" *
. *****************************
. 
. 
. 
. 
. 
. *----- 
. *-------------------------------
. *---  F. Table F9
. 
. 
. preserve

. 
. matrix EN = J(10,3,0)

. matrix colnames EN = "Likert"  "Likert +"  "QVSR" 

. matrix rownames EN = "Same sex right to adopt" ///
> "Make it difficult to buy gun" ///
> "Wall on the US Border" ///
> "Paid leave " ///
> "Preferential hiring of blacks" ///
> "Pay women and men the same" ///
> "Minimum wage to 15 an hour" ///
> "Ban on abortion" ///
> "Cap on federal spending" ///
> "Regulation for environment"

. 
. 
. 
. 
. local variables gay gun wall paidL AA gender minW abortion deficit enviro 

. foreach var in `variables' {
  2. entropyetc votes_`var'w1 if method == 1 , list gen(2=entro1_`var')
  3. }

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.616    5.034     0.231       4.334 |
  +-----------------------------------------------------------+
(2,615 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.570    4.805     0.271       3.696 |
  +-----------------------------------------------------------+
(2,613 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.575    4.832     0.252       3.967 |
  +-----------------------------------------------------------+
(2,614 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.588    4.894     0.242       4.132 |
  +-----------------------------------------------------------+
(2,614 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.581    4.858     0.249       4.011 |
  +-----------------------------------------------------------+
(2,613 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.004    2.728     0.520       1.923 |
  +-----------------------------------------------------------+
(2,612 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.668    5.299     0.220       4.536 |
  +-----------------------------------------------------------+
(2,612 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.556    4.742     0.260       3.851 |
  +-----------------------------------------------------------+
(2,614 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.468    4.343     0.283       3.528 |
  +-----------------------------------------------------------+
(2,613 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all          7       1.610    5.000     0.237       4.223 |
  +-----------------------------------------------------------+
(2,612 missing values generated)

. 
. local variables gay gun wall paidL AA gender minW abortion deficit enviro 

. foreach var in `variables' {
  2. entropyetc votes_`var'w1 if method == 2 , list gen(2=entro2_`var')
  3. }

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       2.158    8.650     0.134       7.450 |
  +-----------------------------------------------------------+
(2,621 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       1.995    7.351     0.160       6.262 |
  +-----------------------------------------------------------+
(2,622 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       2.177    8.822     0.123       8.143 |
  +-----------------------------------------------------------+
(2,621 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       2.035    7.649     0.155       6.444 |
  +-----------------------------------------------------------+
(2,620 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       1.942    6.973     0.186       5.371 |
  +-----------------------------------------------------------+
(2,620 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       1.708    5.519     0.223       4.490 |
  +-----------------------------------------------------------+
(2,622 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       2.147    8.557     0.133       7.542 |
  +-----------------------------------------------------------+
(2,620 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       2.130    8.411     0.133       7.543 |
  +-----------------------------------------------------------+
(2,622 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       1.987    7.290     0.162       6.171 |
  +-----------------------------------------------------------+
(2,623 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         11       1.836    6.271     0.188       5.324 |
  +-----------------------------------------------------------+
(2,621 missing values generated)

. 
. local variables gay gun wall paidL AA gender minW abortion deficit enviro 

. foreach var in `variables' {
  2. entropyetc votes_`var'w1 if method == 3 , list gen(2=entro3_`var')
  3. }

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         15       2.241    9.402     0.131       7.654 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         15       2.410   11.133     0.102       9.801 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         15       2.522   12.459     0.089      11.281 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         13       2.031    7.619     0.165       6.054 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         15       2.268    9.659     0.120       8.331 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         13       2.031    7.623     0.157       6.364 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         15       2.246    9.454     0.127       7.850 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         15       2.489   12.043     0.093      10.777 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         13       2.045    7.729     0.158       6.343 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

  +-----------------------------------------------------------+
  |       distinct   Shannon H   exp(H)   Simpson   1/Simpson |
  |-----------------------------------------------------------|
  | all         14       2.121    8.336     0.149       6.723 |
  +-----------------------------------------------------------+
(2,706 missing values generated)

. 
. collapse (mean) entro1_gay-entro3_enviro

. mkmat entro1_gay-entro3_enviro, mat(entropy)

.         
.         
. local i=1

. while `i' <=10 {
  2. matrix EN[`i',1] = entropy[1, `i']      
  3. local i = `i'+1 
  4. }

. 
.         
. local i=11

. while `i' <=20 {
  2. local j= `i'-10
  3. matrix EN[`j',2] = entropy[1, `i']      
  4. local i = `i'+1
  5. }

. 
. 
. local i=21

. while `i' <=30 {
  2. local j= `i'-20
  3. matrix EN[`j',3] = entropy[1, `i']      
  4. local i = `i'+1
  5. }

. 
. cd "$pathtab"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/tab

. 
. esttab matrix(EN, fmt("2 2")) using entropy.tex, replace ///
> title(Entropy Scores \label{entropy}) 
(output written to entropy.tex)

. 
. restore

. 
. 
. **********************************************************
. * OUTPUT TAB F9                                                          *
. * PLEASE RUN CORRESPONDING LATEX FILE AVAILABLE IN "TAB" *
. **********************************************************
. 
. 
. 
. *----- 
. *-------------------------------
. *---  F2 Scatter Plots and Histograms 
. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. 
. local issues LPP3

. foreach j in `issues' {
  2. 
. egen mean_wall_inL_`j' = mean(don_C_wall_in) if method == 1 , by(votes_wall_inw1N)
  3. egen mean_wall_inLp_`j' = mean(don_C_wall_in) if method == 2 , by(votes_wall_inw1`j'N)
  4. egen mean_wall_inQV_`j' = mean(don_C_wall_in) if method == 3 , by(votes_wall_inw1N)
  5. 
. egen count_wall_in_`j' = count(votes_wall_inw1`j'N), by(method votes_wall_inw1`j'N)
  6. } 
(2,608 missing values generated)
(2,614 missing values generated)
(2,706 missing values generated)

. 
. 
. *** drop ouliers for LPP3
. 
. local issues LPP3

. foreach j in `issues' {
  2.         
. preserve
  3. 
. drop if mean_wall_inLp_`j' < -40
  4. 
. tw (lfitci don_C_wall_in votes_wall_inw1`j'N if method == 2,  level(95) clp(dash_dot) clc(black) yline(-20) yline(20)) ///
> (scatter mean_wall_inLp_`j' votes_wall_inw1`j'N if method == 2 [w=count_wall_in_`j'],  mc(black)  msymbol(oh) ///
> ylabel(-40(20)40 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("Donation (amount)", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert+")  legend(off) )
  5. graph save "wall_in_fig2`j'.gph", replace 
  6. 
. restore
  7. 
. 
. }
(0 observations deleted)
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file wall_in_fig2LPP3.gph not found)
file wall_in_fig2LPP3.gph saved

. 
. 
. tw (lfitci don_C_wall_in votes_wall_inw1N if method == 1,  level(95) clp(shortdash) clc(black) yline(-20) yline(20)) ///
> (scatter mean_wall_inL_LPP3 votes_wall_inw1N if method == 1 [w=count_wall_in_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-40(20)40 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "wall_in_fig2L.gph", replace 
(file wall_in_fig2L.gph not found)
file wall_in_fig2L.gph saved

. 
. 
. tw (lfitci don_C_wall_in votes_wall_inw1N if method == 3,  level(95) clp(shortdash) clc(black) yline(-20) yline(20)) ///
> (scatter mean_wall_inQV_LPP3 votes_wall_inw1N if method == 3 [w=count_wall_in_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-40(20)40 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("QVSR")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "wall_in_fig2QV.gph", replace 
(file wall_in_fig2QV.gph not found)
file wall_in_fig2QV.gph saved

. 
. 
. ***---  bottom figures
. 
. local issues LPP3

. foreach j in `issues' {
  2.                 
. gr tw (hist votes_wall_inw1`j'N if  method == 2, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) ///
> ytitle("Density", size(medsmall)) 
  3. graph save "wall_in`j'w1N.gph", replace 
  4. 
. }
(file wall_inLPP3w1N.gph not found)
file wall_inLPP3w1N.gph saved

. 
. 
. 
. gr tw (hist votes_wall_inw1N if  method == 1, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 

. graph save "wall_inLw1N.gph", replace 
(file wall_inLw1N.gph not found)
file wall_inLw1N.gph saved

.         
. gr tw (hist votes_wall_inw1N if  method == 3, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 

. graph save "wall_inQVw1N.gph", replace 
(file wall_inQVw1N.gph not found)
file wall_inQVw1N.gph saved

. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. graph combine  "wall_in_fig2LPP3.gph"  "wall_in_fig2L.gph" "wall_in_fig2QV.gph"  ///
>  "wall_inLPP3w1N.gph" "wall_inLw1N.gph" "wall_inQVw1N.gph" , ///
> col(3) title("Immigration Donations and Opposition to Border Wall", size(med)) 
(note:  named style med not found in class gsize, default attributes used)

. graph export "$pathfig/AppendixF2_1.pdf", replace
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/AppendixF2_1.pdf saved as PDF format

. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. erase "wall_in_fig2LPP3.gph"  

. erase "wall_in_fig2L.gph" 

. erase "wall_in_fig2QV.gph"  

. erase "wall_inLPP3w1N.gph" 

. erase "wall_inLw1N.gph" 

. erase "wall_inQVw1N.gph"

. 
. *** abortion letter wrriting 
. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. 
. local issues LPP3

. foreach j in `issues' {
  2. 
. egen mean_abortionL_`j' = mean(writing_abortionNST ) if method == 1 , by(votes_abortionw1N)
  3. egen mean_abortionLp_`j' = mean(writing_abortionNST ) if method == 2 , by(votes_abortionw1`j'N)
  4. egen mean_abortionQV_`j' = mean(writing_abortionNST ) if method == 3 , by(votes_abortionw1N)
  5. 
. egen count_abortion_`j' = count(votes_abortionw1`j'N), by(method votes_abortionw1`j'N)
  6. } 
(2,608 missing values generated)
(2,615 missing values generated)
(2,706 missing values generated)

. 
. 
. local issues LPP3

. foreach j in `issues' {
  2.         
. preserve
  3. 
. tw (qfitci writing_abortionNST votes_abortionw1`j'N if method == 2,  level(95) clp(dash_dot) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_abortionLp_`j' votes_abortionw1`j'N if method == 2 [w=count_abortion_`j'],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1.8  , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("Writing length (stand.)", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert +")  legend(off) )
  4. graph save "abortion_fig2`j'.gph", replace 
  5. 
. restore
  6. 
. 
. }
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file abortion_fig2LPP3.gph not found)
file abortion_fig2LPP3.gph saved

. 
. 
. tw (qfitci writing_abortionNST votes_abortionw1N if method == 1,  level(95) clp(shortdash) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_abortionL_LPP3 votes_abortionw1N if method == 1 [w=count_abortion_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1.8 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "abortion_fig2L.gph", replace 
(file abortion_fig2L.gph not found)
file abortion_fig2L.gph saved

. 
. 
. tw (qfitci writing_abortionNST votes_abortionw1N if method == 3,  level(95) clp(shortdash) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_abortionQV_LPP3 votes_abortionw1N if method == 3 [w=count_abortion_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1.8 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("QVSR")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "abortion_fig2QV.gph", replace 
(file abortion_fig2QV.gph not found)
file abortion_fig2QV.gph saved

. 
. 
. ***---  bottom figures
. 
. local issues LPP3

. foreach j in `issues' {
  2.                 
. gr tw (hist votes_abortionw1`j'N if  method == 2, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) ///
> ytitle("Density", size(medsmall)) 
  3. graph save "abortion`j'w1N.gph", replace 
  4. 
. }
(file abortionLPP3w1N.gph not found)
file abortionLPP3w1N.gph saved

. 
. 
. 
. gr tw (hist votes_abortionw1N if  method == 1, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 

. graph save "abortionLw1N.gph", replace 
(file abortionLw1N.gph not found)
file abortionLw1N.gph saved

.         
. gr tw (hist votes_abortionw1N if  method == 3, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 

. graph save "abortionQVw1N.gph", replace 
(file abortionQVw1N.gph not found)
file abortionQVw1N.gph saved

. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. graph combine  "abortion_fig2LPP3.gph"  "abortion_fig2L.gph" "abortion_fig2QV.gph"  ///
>  "abortionLPP3w1N.gph" "abortionLw1N.gph" "abortionQVw1N.gph" , ///
> col(3) title("Length of Writing: Abortion Bill and Support for Abortion", size(med)) 
(note:  named style med not found in class gsize, default attributes used)

. graph export "$pathfig/AppendixF2_2.pdf", replace
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/AppendixF2_2.pdf saved as PDF format

. 
. erase "abortion_fig2LPP3.gph"

. erase "abortion_fig2L.gph"

. erase "abortion_fig2QV.gph"

. erase "abortionLPP3w1N.gph" 

. erase "abortionLw1N.gph" 

. erase "abortionQVw1N.gph"

. 
. 
. *** min wage letter wrriting 
. 
. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. 
. local issues LPP3

. foreach j in `issues' {
  2. 
. egen mean_minWL_`j' = mean(writing_minWNST ) if method == 1 , by(votes_minWw1N)
  3. egen mean_minWLp_`j' = mean(writing_minWNST ) if method == 2 , by(votes_minWw1`j'N)
  4. egen mean_minWQV_`j' = mean(writing_minWNST ) if method == 3 , by(votes_minWw1N)
  5. 
. egen count_minW_`j' = count(votes_minWw1`j'N), by(method votes_minWw1`j'N)
  6. } 
(2,608 missing values generated)
(2,617 missing values generated)
(2,712 missing values generated)

. 
. 
. 
. local issues LPP3

. foreach j in `issues' {
  2.         
. preserve
  3. 
. tw (qfitci writing_minWNST votes_minWw1`j'N if method == 2,  level(95) clp(dash_dot) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_minWLp_`j' votes_minWw1`j'N if method == 2 [w=count_minW_`j'],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1  , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("Writing length (stand.)", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert +")  legend(off) )
  4. graph save "minW_fig2`j'.gph", replace 
  5. 
. restore
  6. 
. 
. }
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file minW_fig2LPP3.gph not found)
file minW_fig2LPP3.gph saved

. 
. 
. tw (qfitci writing_minWNST votes_minWw1N if method == 1,  level(95) clp(shortdash) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_minWL_LPP3 votes_minWw1N if method == 1 [w=count_minW_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "minW_fig2L.gph", replace 
(file minW_fig2L.gph not found)
file minW_fig2L.gph saved

. 
. 
. tw (qfitci writing_minWNST votes_minWw1N if method == 3,  level(95) clp(shortdash) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_minWQV_LPP3 votes_minWw1N if method == 3 [w=count_minW_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("QVSR")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "minW_fig2QV.gph", replace 
(file minW_fig2QV.gph not found)
file minW_fig2QV.gph saved

. 
. 
. ***---  bottom figures
. 
. local issues LPP3

. foreach j in `issues' {
  2.                 
. gr tw (hist votes_minWw1`j'N if  method == 2, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) ///
> ytitle("Density", size(medsmall)) 
  3. graph save "minW`j'w1N.gph", replace 
  4. 
. }
(file minWLPP3w1N.gph not found)
file minWLPP3w1N.gph saved

. 
. 
. 
. gr tw (hist votes_minWw1N if  method == 1, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 

. graph save "minWLw1N.gph", replace 
(file minWLw1N.gph not found)
file minWLw1N.gph saved

.         
. gr tw (hist votes_minWw1N if  method == 3, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 

. graph save "minWQVw1N.gph", replace 
(file minWQVw1N.gph not found)
file minWQVw1N.gph saved

. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. graph combine  "minW_fig2LPP3.gph"  "minW_fig2L.gph" "minW_fig2QV.gph"  ///
>  "minWLPP3w1N.gph" "minWLw1N.gph" "minWQVw1N.gph" , ///
> col(3) title("Length of Writing: minW Bill and Support for minW", size(med)) 
(note:  named style med not found in class gsize, default attributes used)

. graph export "$pathfig/AppendixF2_3.pdf", replace
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/AppendixF2_3.pdf saved as PDF format

. 
. erase "minW_fig2LPP3.gph"  

. erase "minW_fig2L.gph"

. erase "minW_fig2QV.gph"  

. erase "minWLPP3w1N.gph"

. erase "minWLw1N.gph" 

. erase "minWQVw1N.gph" 

. 
. 
. *** punishment (1) punish_FaST 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. 
. egen mean_punishL_LPP3 = mean(punish_FaST ) if method == 1 , by(diffLPP3N)
(2,608 missing values generated)

. egen mean_punishLp_LPP3 = mean(punish_FaST ) if method == 2 , by(diffLPP3N)
(2,624 missing values generated)

. egen mean_punishQV_LPP3 = mean(punish_FaST ) if method == 3 , by(diffLPP3N)
(2,707 missing values generated)

. 
. egen count_punish_LPP3 = count(diffLPP3N), by(method diffLPP3N)

. 
. 
. tw (lfitci punish_FaST diffLPP3N if method == 2,  level(95) clp(dash_dot) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_punishLp_LPP3 diffLPP3N if method == 2 [w=count_punish_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1  , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("Amount taken (stand.)", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert +")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "punish1_fig2LPP3.gph", replace 
(file punish1_fig2LPP3.gph not found)
file punish1_fig2LPP3.gph saved

. 
. 
. tw (lfitci punish_FaST diffLPP3N if method == 1,  level(95) clp(shortdash) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_punishL_LPP3 diffLPP3N if method == 1 [w=count_punish_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "punish1_fig2L.gph", replace 
(file punish1_fig2L.gph not found)
file punish1_fig2L.gph saved

. 
. 
. tw (lfitci punish_FaST diffLPP3N if method == 3,  level(95) clp(shortdash) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_punishQV_LPP3 diffLPP3N if method == 3 [w=count_punish_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-0.8(0.2)1 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("QVSR")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "punish1_fig2QV.gph", replace 
(file punish1_fig2QV.gph not found)
file punish1_fig2QV.gph saved

. 
. 
. ***---  bottom figures
. 
.                 
. gr tw (hist diffLPP3N if  method == 2, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) ///
> ytitle("Density", size(medsmall)) 

. graph save "punishLPP3w1N.gph", replace 
(file punishLPP3w1N.gph not found)
file punishLPP3w1N.gph saved

. 
. 
. gr tw (hist diffLPP3N if  method == 1, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 

. graph save "punishLw1N.gph", replace 
(file punishLw1N.gph not found)
file punishLw1N.gph saved

.         
. gr tw (hist diffLPP3N if  method == 3, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 

. graph save "punishQVw1N.gph", replace 
(file punishQVw1N.gph not found)
file punishQVw1N.gph saved

. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. graph combine  "punish1_fig2LPP3.gph"  "punish1_fig2L.gph" "punish1_fig2QV.gph"  ///
>  "punishLPP3w1N.gph" "punishLw1N.gph" "punishQVw1N.gph" , ///
> col(3) title("Amount Taken in DG and Difference Votes Gun - Immi", size(med)) 
(note:  named style med not found in class gsize, default attributes used)

. graph export "$pathfig/AppendixF2_4.pdf", replace
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/AppendixF2_4.pdf saved as PDF format

. 
. erase "punish1_fig2LPP3.gph"  

. erase "punish1_fig2L.gph" 

. erase "punish1_fig2QV.gph"  

. 
. 
. 
. 
. *** punishment (2) proportionST 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. 
. egen mean_punish2L_LPP3 = mean(proportionST) if method == 1 , by(diffLPP3N)
(2,608 missing values generated)

. egen mean_punish2Lp_LPP3 = mean(proportionST) if method == 2 , by(diffLPP3N)
(2,624 missing values generated)

. egen mean_punish2QV_LPP3 = mean(proportionST) if method == 3 , by(diffLPP3N)
(2,707 missing values generated)

. 
. 
. tw (lfitci proportionST  diffLPP3N if method == 2,  level(95) clp(dash_dot) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_punish2Lp_LPP3 diffLPP3N if method == 2 [w=count_punish_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-1.8(0.2)1  , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("% taken (stand.)", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert +")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "punish2_fig2LPP3.gph", replace 
(file punish2_fig2LPP3.gph not found)
file punish2_fig2LPP3.gph saved

. 
. 
. tw (lfitci proportionST  diffLPP3N if method == 1,  level(95) clp(shortdash) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_punish2L_LPP3 diffLPP3N if method == 1 [w=count_punish_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-1.8(0.2)1 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "punish2_fig2L.gph", replace 
(file punish2_fig2L.gph not found)
file punish2_fig2L.gph saved

. 
. 
. tw (lfitci proportionST  diffLPP3N if method == 3,  level(95) clp(shortdash) clc(black) yline(-0.4) yline(0.4)) ///
> (scatter mean_punish2QV_LPP3 diffLPP3N if method == 3 [w=count_punish_LPP3],  mc(black)  msymbol(oh) ///
> ylabel(-1.8(0.2)1 , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("QVSR")  legend(off) )
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)

. graph save "punish2_fig2QV.gph", replace 
(file punish2_fig2QV.gph not found)
file punish2_fig2QV.gph saved

. 
. 
. 
. 
. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. graph combine  "punish2_fig2LPP3.gph"  "punish2_fig2L.gph" "punish2_fig2QV.gph"  ///
>  "punishLPP3w1N.gph" "punishLw1N.gph" "punishQVw1N.gph" , ///
> col(3) title("Perc. Taken in DG and Difference Votes Gun - Immi", size(med)) 
(note:  named style med not found in class gsize, default attributes used)

. graph export "$pathfig/AppendixF2_5.pdf", replace
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/AppendixF2_5.pdf saved as PDF format

. 
. erase "punish2_fig2LPP3.gph"  

. erase "punish2_fig2L.gph" 

. erase "punish2_fig2QV.gph"  

. erase "punishLPP3w1N.gph" 

. erase "punishLw1N.gph" 

. erase "punishQVw1N.gph"

. 
. 
. **************************************************************
. * OUTPUT APPENDIX F2                                                     *
. * PLEASE  See "AppendixF2_1"  "AppendixF2_2"  "AppendixF2_3" *
. * "AppendixF2_4"  "AppendixF2_5" IN "FIG"                    *
. **************************************************************
. 
. 
. *----- 
. *-------------------------------
. *---  Appendix G - Material Self-Interest - Figures 
. 
. ******load dataset
. 
. use "$pathout/dataset_final.dta", replace 

. 
. 
. recode sex (1 = 0) (2 =1), gen(Gender)
(3,940 differences between sex and Gender)

. rename black Black

. rename votes_AAw1LPP3N votes_Blackw1LPP3N

. rename votes_gunw1LPP3N votes_gunOaw1LPP3N

. 
. *** see table in text for coding 
. recode minW ( 1 2 = 1) (3 = 0)
(1,770 changes made to minW)

. recode gunOa (2 3 = 0) (4 = 1)
(3,940 changes made to gunOa)

. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. local issues Black gunOa minW

. foreach j in `issues' {
  2. gr tw (hist votes_Blackw1LPP3N if  method == 2, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) ///
> ytitle("Density", size(medsmall)) 
  3. graph save "`j'LPP3w1N.gph", replace 
  4. 
. gr tw (hist votes_Blackw1LPP3N if  method == 1, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 
  5. graph save "`j'Lw1N.gph", replace 
  6.         
. gr tw (hist votes_Blackw1LPP3N if  method == 3, frac ///
> discrete bfcolor(teal)  blcolor(white) title("")) ,  leg(off)  ///
> graphr(fc(white) lc(white) ifc(white) ilc(white)) title("", size(medium)) yti("") xti("") ///
> ysc(range(0 .5)) ylab(0(.1).5)  ///
> xsc(range(0 1)) xlab(0(0.1)1) fysize(20) 
  7. graph save "`j'QVw1N.gph", replace 
  8. }
(file BlackLPP3w1N.gph not found)
file BlackLPP3w1N.gph saved
(file BlackLw1N.gph not found)
file BlackLw1N.gph saved
(file BlackQVw1N.gph not found)
file BlackQVw1N.gph saved
(file gunOaLPP3w1N.gph not found)
file gunOaLPP3w1N.gph saved
(file gunOaLw1N.gph not found)
file gunOaLw1N.gph saved
(file gunOaQVw1N.gph not found)
file gunOaQVw1N.gph saved
(file minWLPP3w1N.gph not found)
file minWLPP3w1N.gph saved
(file minWLw1N.gph not found)
file minWLw1N.gph saved
(file minWQVw1N.gph not found)
file minWQVw1N.gph saved

. 
. 
. 
. local issues Black minW gunOa

. foreach j in `issues' {
  2. 
. egen mean_`j'L_LPP3 = mean(`j') if method == 1 , by(votes_`j'w1LPP3N)
  3. egen mean_`j'Lp_LPP3 = mean(`j') if method == 2 , by(votes_`j'w1LPP3N)
  4. egen mean_`j'QV_LPP3 = mean(`j') if method == 3 , by(votes_`j'w1LPP3N)
  5. 
. egen count_`j'LPP3 = count(votes_`j'w1LPP3N), by(method votes_`j'w1LPP3N)
  6. 
. }
(2,608 missing values generated)
(2,614 missing values generated)
(2,706 missing values generated)
(2,608 missing values generated)
(2,614 missing values generated)
(2,707 missing values generated)
(2,608 missing values generated)
(2,614 missing values generated)
(2,706 missing values generated)

. 
. 
. local issues Black minW gunOa

. foreach j in `issues' {
  2. 
. tw (lfitci `j' votes_`j'w1LPP3N if method == 2,  level(95) clp(dash_dot) clc(black) ) ///
> (scatter mean_`j'Lp_LPP3 votes_`j'w1LPP3N if method == 2 [w=count_`j'LPP3],  mc(black)  msymbol(oh) ///
> ylabel(0(0.2)1  , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("Percent affected", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert +")  legend(off) )
  3. graph save "`j'_fig4LPP3.gph", replace 
  4. 
. 
. tw (lfitci `j' votes_`j'w1LPP3N if method == 1,  level(95) clp(dash_dot) clc(black) ) ///
> (scatter mean_`j'L_LPP3 votes_`j'w1LPP3N if method == 1 [w=count_`j'LPP3],  mc(black)  msymbol(oh) ///
> ylabel(0(0.2)1  , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("Likert")  legend(off) )
  5. graph save "`j'_fig4L.gph", replace 
  6. 
. 
. tw (lfitci `j' votes_`j'w1LPP3N if method == 3,  level(95) clp(dash_dot) clc(black) ) ///
> (scatter mean_`j'QV_LPP3 votes_`j'w1LPP3N if method == 3 [w=count_`j'LPP3],  mc(black)  msymbol(oh) ///
> ylabel(0(0.2)1  , angle(horizontal)labsize(medsmall)) yline(0) xlabel(0(0.1)1, angle(horizontal)labsize(medsmall)) ///
> ytitle("", size(medsmall)) xtitle("", size(medsmall)) ///
> title("QVSR")  legend(off) )
  7. graph save "`j'_fig4QV.gph", replace 
  8. 
. }
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file Black_fig4LPP3.gph not found)
file Black_fig4LPP3.gph saved
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file Black_fig4L.gph not found)
file Black_fig4L.gph saved
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file Black_fig4QV.gph not found)
file Black_fig4QV.gph saved
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file minW_fig4LPP3.gph not found)
file minW_fig4LPP3.gph saved
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file minW_fig4L.gph not found)
file minW_fig4L.gph saved
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file minW_fig4QV.gph not found)
file minW_fig4QV.gph saved
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file gunOa_fig4LPP3.gph not found)
file gunOa_fig4LPP3.gph saved
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file gunOa_fig4L.gph not found)
file gunOa_fig4L.gph saved
(analytic weights assumed)
(analytic weights assumed)
(analytic weights assumed)
(file gunOa_fig4QV.gph not found)
file gunOa_fig4QV.gph saved

. 
. cd "$pathtemp"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. 
. 
. local issues  gunOa

. foreach j in `issues' {
  2. graph combine  "`j'_fig4LPP3.gph"  "`j'_fig4L.gph" "`j'_fig4QV.gph"  ///
> "`j'LPP3w1N.gph" "`j'Lw1N.gph" "`j'QVw1N.gph" , ///
> col(3) title("% Does NOT Own a Gun and Support for Gun Control", size(med)) 
  3. graph export "$pathfig/AppendixG_1.pdf", replace
  4.         
.         
. }
(note:  named style med not found in class gsize, default attributes used)
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/AppendixG_1.pdf saved as PDF format

. 
. local issues Black 

. foreach j in `issues' {
  2. graph combine  "`j'_fig4LPP3.gph"  "`j'_fig4L.gph" "`j'_fig4QV.gph"  ///
> "`j'LPP3w1N.gph" "`j'Lw1N.gph" "`j'QVw1N.gph" , ///
> col(3) title("% Black and Support for Affirmative Action", size(med)) 
  3. graph export "$pathfig/AppendixG_2.pdf", replace
  4.         
.         
. }
(note:  named style med not found in class gsize, default attributes used)
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/AppendixG_2.pdf saved as PDF format

. 
. local issues minW 

. foreach j in `issues' {
  2. graph combine  "`j'_fig4LPP3.gph"  "`j'_fig4L.gph" "`j'_fig4QV.gph"  ///
> "`j'LPP3w1N.gph" "`j'Lw1N.gph" "`j'QVw1N.gph" , ///
> col(3) title("% At or Close to Min Wage and Support for Min Wage Increase", size(med)) 
  3. graph export "$pathfig/AppendixG_3.pdf", replace
  4.         
.         
. }
(note:  named style med not found in class gsize, default attributes used)
file /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/fig/AppendixG_3.pdf saved as PDF format

. 
. ** erase
. 
. local issues Black 

. foreach j in `issues' {
  2. cd "$pathtemp"
  3. erase  "`j'_fig4LPP3.gph"  
  4. erase "`j'_fig4L.gph" 
  5. erase "`j'_fig4QV.gph"  
  6. erase "`j'LPP3w1N.gph" 
  7. erase "`j'Lw1N.gph" 
  8. erase "`j'QVw1N.gph" 
  9.         
. }
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/temp

. 
. local issues minW 

. foreach j in `issues' {
  2. erase  "`j'_fig4LPP3.gph" 
  3. erase  "`j'_fig4L.gph" 
  4. erase  "`j'_fig4QV.gph" 
  5. erase  "`j'LPP3w1N.gph" 
  6. erase  "`j'Lw1N.gph" 
  7. erase  "`j'QVw1N.gph" 
  8. }

. 
. local issues  gunOa

. foreach j in `issues' {
  2. erase  "`j'_fig4LPP3.gph" 
  3. erase  "`j'_fig4L.gph" 
  4. erase  "`j'_fig4QV.gph" 
  5. erase  "`j'LPP3w1N.gph" 
  6. erase  "`j'Lw1N.gph"
  7. erase  "`j'QVw1N.gph" 
  8. }

. 
. 
. **************************************************************
. * OUTPUT APPENDIX F2 Figures                                             *
. * PLEASE  See "AppendixG_1"  "AppendixG_2"  "AppendixG_3"    *
. *  IN "FIG"                                                  *
. **************************************************************
. 
. 
. 
. 
. *----- 
. *-------------------------------
. *---  Appendix G - Material Self-Interest - Tab G11
. 
. 
. ******load dataset
. 
. use "$pathout/dataset_final.dta", replace 

. 
. 
. recode sex (1 = 0) (2 =1), gen(Gender)
(3,940 differences between sex and Gender)

. rename black Black

. rename votes_AAw1LPP3N votes_Blackw1LPP3N

. rename votes_gunw1LPP3N votes_gunOaw1LPP3N

. 
. *** see table in text for coding 
. recode minW ( 1 2 = 1) (3 = 0)
(1,770 changes made to minW)

. recode gunOa (2 3 = 0) (4 = 1)
(3,940 changes made to gunOa)

. 
. 
. 
. matrix EV_tabG11 = J(2,3,0)

. matrix colnames EV_tabG11 = "QVSR vs Likert" "Likert+ vs Likert" "QVSR vs LIkert+" 

. matrix rownames EV_tabG11 = "F test" "Prob > F "

. 
. 
. 
. preserve

. 
. drop if method == 2
(1,350 observations deleted)

. 
. recode method (1 = 0) (3 = 1)
(2,614 changes made to method)

. 
. qui sureg (Gender c.votes_genderw1LPP3N##i.method i.block) ///
> (child2 c.votes_paidLw1LPP3N##i.method i.block) ///
> (gunOa c.votes_gunOaw1LPP3N##i.method i.block) ///
> (Black c.votes_Blackw1LPP3N##i.method i.block) ///
> (minW c.votes_minWw1LPP3N##i.method i.block) /// 
>  , small dfk coeflegend 

.  estimates store SUREG_LQV

. 
. 
. 
. test _b[Gender:1.method#c.votes_genderw1LPP3N] + ///
> _b[child2:1.method#c.votes_paidLw1LPP3N]+ ///
> _b[gunOa:1.method#c.votes_gunOaw1LPP3N] + ///
> _b[Black:1.method#c.votes_Blackw1LPP3N] + ///
> _b[minW:1.method#c.votes_minWw1LPP3N] = 0 

 ( 1)  [Gender]1.method#c.votes_genderw1LPP3N + [child2]1.method#c.votes_paidLw1LPP3N + [gunOa]1.method#c.votes_gunOaw1LPP3N + [Black]1.method#c.votes_Blackw1LPP3N + [minW]1.method#c.votes_minWw1LPP3N = 0

       F(  1,  6520) =   51.96
            Prob > F =    0.0000

. matrix EV_tabG11[1,1] = r(F)

. matrix EV_tabG11[2,1] = r(p)

. 
. restore

. 
. 
. preserve

. 
. drop if method == 1
(1,356 observations deleted)

. 
. recode method (2 = 0) (3 = 1)
(2,608 changes made to method)

. 
. qui sureg (Gender c.votes_genderw1LPP3N##i.method i.block) ///
> (child2 c.votes_paidLw1LPP3N##i.method i.block) ///
> (gunOa c.votes_gunOaw1LPP3N##i.method i.block) ///
> (Black c.votes_Blackw1LPP3N##i.method i.block) ///
> (minW c.votes_minWw1LPP3N##i.method i.block) /// 
>  , small dfk coeflegend 

.  estimates store SUREG_QVLik

. 
. 
. 
. test _b[Gender:1.method#c.votes_genderw1LPP3N] + ///
> _b[child2:1.method#c.votes_paidLw1LPP3N]+ ///
> _b[gunOa:1.method#c.votes_gunOaw1LPP3N] + ///
> _b[Black:1.method#c.votes_Blackw1LPP3N] + ///
> _b[minW:1.method#c.votes_minWw1LPP3N] = 0 

 ( 1)  [Gender]1.method#c.votes_genderw1LPP3N + [child2]1.method#c.votes_paidLw1LPP3N + [gunOa]1.method#c.votes_gunOaw1LPP3N + [Black]1.method#c.votes_Blackw1LPP3N + [minW]1.method#c.votes_minWw1LPP3N = 0

       F(  1,  6575) =    8.52
            Prob > F =    0.0035

. matrix EV_tabG11[1,2] = r(F)

. matrix EV_tabG11[2,2] = r(p)

. 
. 
. restore

. 
. 
. preserve

. 
. drop if method == 3
(1,258 observations deleted)

. 
. recode method (1 = 0) (2 = 1)
(2,706 changes made to method)

. 
. qui sureg (Gender c.votes_genderw1LPP3N##i.method i.block) ///
> (child2 c.votes_paidLw1LPP3N##i.method i.block) ///
> (gunOa c.votes_gunOaw1LPP3N##i.method i.block) ///
> (Black c.votes_Blackw1LPP3N##i.method i.block) ///
> (minW c.votes_minWw1LPP3N##i.method i.block) /// 
>  , small dfk coeflegend 

.  estimates store SUREG_LikL

. 
. 
. 
. test _b[Gender:1.method#c.votes_genderw1LPP3N] + ///
> _b[child2:1.method#c.votes_paidLw1LPP3N]+ ///
> _b[gunOa:1.method#c.votes_gunOaw1LPP3N] + ///
> _b[Black:1.method#c.votes_Blackw1LPP3N] + ///
> _b[minW:1.method#c.votes_minWw1LPP3N] = 0 

 ( 1)  [Gender]1.method#c.votes_genderw1LPP3N + [child2]1.method#c.votes_paidLw1LPP3N + [gunOa]1.method#c.votes_gunOaw1LPP3N + [Black]1.method#c.votes_Blackw1LPP3N + [minW]1.method#c.votes_minWw1LPP3N = 0

       F(  1,  6750) =   30.03
            Prob > F =    0.0000

. matrix EV_tabG11[1,3] = r(F)

. matrix EV_tabG11[2,3] = r(p)

. 
. restore

. 
. 
. cd "$pathtab"
/Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/tab

. 
. 
. esttab SUREG_LQV SUREG_LikL SUREG_QVLik  using sureg_mat.tex, replace ///
> style(tex) cells(b(star fmt(2)) se(par(( )) fmt(2))) starlevels(* .05 ** .01 *** .001) ///
> stats(N, fmt(0)) ///
> title(Differences in Coefficient Size (SUR): Exposure Outcomes \label{suregmat}) ///
> varlabels(_cons \_cons ///
> 1.method#c.votes_genderw1LPP3N Gender ///
> 1.method#c.votes_paidLw1LPP3N Prox_childB ///
> 1.method#c.votes_gunOaw1LPP3N No_Gun ///
> 1.method#c.votes_Blackw1LPP3N Black ///
> 1.method#c.votes_minWw1LPP3N Min_wage) /// 
> keep(1.method#c.votes_genderw1LPP3N ///
> 1.method#c.votes_paidLw1LPP3N 1.method#c.votes_gunOaw1LPP3N ///
> 1.method#c.votes_Blackw1LPP3N 1.method#c.votes_minWw1LPP3N) 
(output written to sureg_mat.tex)

. 
. 
. esttab  matrix(EV_tabG11, fmt("0 3")) using sureg_mat2.tex, replace
(output written to sureg_mat2.tex)

. 
. 
. **********************************************************
. * OUTPUT TAB G11                                                         *
. * PLEASE RUN CORRESPONDING LATEX FILE AVAILABLE IN "TAB" *
. **********************************************************
. 
. 
. erase "$pathtemp/ids_dataset_final.dta" 

. erase "$pathtemp/ids_wave2.dta"

. 
. log close
      name:  <unnamed>
       log:  /Users/cavaille/Dropbox/CO.who_cares/2.Data and Analyses/7.Replication file PSRM/log_appendix.log
  log type:  text
 closed on:  10 Feb 2024, 12:04:26
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
